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• By considering empty and laden containers, this paper investigates the LSHLPECR. • A MILP (mixed integer linear program) is presented for formulating the LSHLPECR. • In terms of the solution of our MILP, this paper further investigates the CLPP. • PCLPs at various ports are obtained by using inverse optimization. Considering the influence of economies of scale and ship capacity on empty container repositioning, using foldable containers and container leasing may be effective ways to save the operating costs of liner shipping companies. Considering the minimization of the transportation costs of empty and laden containers, this paper studies a liner shipping hub location problem with empty container repositioning (LSHLPECR) considering foldable containers. We propose a mixed integer linear program (MILP) for the formulation of the LSHLPECR. In terms of the optimal solution of our MILP, this paper further explores the container leasing pricing problem (CLPP), where our aim is to obtain perceived container leasing prices (PCLPs) utilizing the inverse optimization technique. Numerical experiments verify the effectiveness of the studied problems. [ABSTRACT FROM AUTHOR]

With the development of intelligent logistics technology, some companies began to use robots instead of humans to load cargo. This paper studies a novel container loading problem based on robotic loader system (CLP-RLS). Different from the existing robot-packable pattern in the literature, the robotic loader system in this paper consists of a depalletizing robot, an automatic telescopic roller line and a loading robot, in which the loading robot will enter the carriage along with the automatic telescopic roller line. In CLP-RLS, it is necessary to consider not only many practical constraints already in the literature, including load balancing, orientation, stability, and multi-drop but also two new constraints related to robotic loader system: pallet continuity constraint (the loading sequence of cargo on the same pallet is continuous) and robot position constraint (the robot can only load cargo incrementally from the front to the back of the truck). Due to the difficulty in modeling CLP-RLS and the large scale of the real-case instances, we present a tree search approach based on wall-building to solve CLP-RLS, intending to find a feasible loading scheme for the RLS to minimize the length required to load all cargo. The effectiveness of the proposed approach is verified through both real-case instances and numerical instances. • A novel container loading problem based on robotic loader system is studied. • A tree search approach based on wall-building is proposed to solve this problem. • The effectiveness of the proposed approach is verified through real-case instances. • The proposed approach can support the decision-making of the truck delivery route. [ABSTRACT FROM AUTHOR]

This paper formulates a mixed integer linear programming (MILP) model to optimize a system of electric vehicle (EV) charging stations. Our methodology introduces a two-stage framework that integrates the first-stage system design problem with a second-stage control problem of the EV charging stations and develops a design and analysis of computer experiments (DACE) based system design optimization solution method. Our DACE approach generates a metamodel to predict revenue from the control problem using multivariate adaptive regression splines (MARS), fit over a binned Latin hypercube (LH) experimental design. Comparing the DACE based approach to using a commercial solver on the MILP, it yields near optimal solutions, provides interpretable profit functions, and significantly reduces computational time for practical application. [ABSTRACT FROM AUTHOR]

Personalized federated learning (PFL) is an exciting approach that allows machine learning (ML) models to be trained on diverse and decentralized sources of data, while maintaining client privacy and autonomy. However, PFL faces several challenges that can deteriorate the performance and effectiveness of the learning process. These challenges include data heterogeneity, communication overhead, model privacy, model drift, client heterogeneity, label noise and imbalance, federated optimization challenges, and client participation and engagement. To address these challenges, researchers are exploring innovative techniques and algorithms that can enable efficient and effective PFL. These techniques include several optimization algorithms. This research survey provides an overview of the challenges and motivations related to the model optimization strategies for PFL, as well as the state-of-the-art (SOTA) methods and algorithms which seek to provide solutions of these challenges. Overall, this survey can be a valuable resource for researchers who are interested in the emerging field of PFL as well as its potential for personalized machine learning in a federated environment. • A Comprehensive Review of Contemporary Approaches in Personalized Federated Learning (PFL). • Categorization and classification of various methods and solutions to provide comprehensive taxonomies. • A diverse array of literature that addresses emerging challenges and cutting-edge solutions in the field. • This paper outlines and proposes new research directions that can be pursued by the interested community. [ABSTRACT FROM AUTHOR]

Web tables are rich sources of structured data for collecting and analyzing data, but there exist various data quality problems such as missing or inconsistent values. Crowdsourcing provides a new solution that leverages the human cognitive ability to clean tables, but existing crowdsourcing-based solutions suffer from cleaning quality. For most crowd are not experts, the difficulty degree of cleaning tasks will seriously affect the cleaning result. To help people clean web tables effectively and efficiently, it is important to reduce the overall difficulty of tasks. In this paper, we introduce a difficulty-aware crowdsourcing task optimization system CrowdDA, which aims to recommend the best task execution order from easy to difficult for crowd and support various kinds of cleaning tasks for web tables. CrowdDA takes both latency and space constraints into account for task optimization and generates the task execution order that minimizes the overall difficulty of tasks under two constraints. Furthermore, CrowdDA adopts partition strategies for large tables to improve system efficiency, and introduces independent task sequence to tolerate crowd's inconsistent answers for system robustness. The experiments based on the real-world datasets demonstrate the performance superiority of CrowdDA for improving the cleaning quality of web tables. • Crowdsourcing provides a solution that uses human cognitive ability to clean tables. • Difficulty degree of cleaning tasks seriously affects the cleaning result. • Task execution order from easy to difficult help people clean tables effectively. • Task execution order needs to satisfy latency and space constraint. • Partition strategy and independent task improve the efficiency of task execution. [ABSTRACT FROM AUTHOR]

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming an already trained Supervised Classification model, post-hoc explanations can be obtained via so-called counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the given Machine Learning classification model. Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single counterfactual explanation. Taking a stakeholder's perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, as in the single-instance single-counterfactual case, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately. Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactual explanations is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers. The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness. • A new model for Explainable Artificial Intelligence is proposed. • Counterfactual Explanations for a full group of individuals are sought. • Sparsity both at individual and group level is achieved. [ABSTRACT FROM AUTHOR]

Pontryagin's maximum principle is one of the most important topics of control theory. It plays a crucial role in solving variational problems in an alternative way. In this paper, Pontryagin's maximum principle is extended in order to solve interval valued control problems (IVCPs). To serve this purpose, firstly all the formal definitions (viz. IVCP, interval ranking and optimizer of IVCP etc.) are presumed. Further, the necessary and sufficient condition, i.e., Pontryagin's principles are extended in interval environment in order to solve an IVCP. The second part of this work presents an application of the proposed extension of Pontryagin's maximum principle for IVCPs. Then, an imperfect production inventory problem is considered by assuming flexibility of all the involved parameters in interval environment. The proposed production problem mainly addresses a joint optimal policy for pricing, dynamic service investment and reworked/salvaged under interval flexibility. The IVCP related to the proposed model is solved using extended Pontryagin's maximum principle and the interval optimization problem corresponding to the model is solved using improved c-r optimization technique. Further, a numerical example is taken and solved using the sparrow search algorithm (SSA) in order to illustrate the proposed theoretical results and validate the proposed model. Finally, sensitivity analyses are carried out w. r. to various system parameters in order to evaluate some implications to the manager. [ABSTRACT FROM AUTHOR]

A metaheuristic-based structural optimization method, whilst being popularly adopted due to its advantages in by-passing gradient function calculations, requires the use of time-consuming advanced analyses for constraint evaluation. To overcome this drawback, the present paper proposes a robust (machine learning-based) optimization method that combines the light gradient boosting machine (LightGBM) with the efficient p-best differential evolution (E p DE) method. In essence, the LightGBM classification model is constructed to assess the constraint (safety and integrity) satisfaction of structures. An efficient framework using a so-called safety parameter is proposed to prevent inaccurate predictions of the LightGBM model. The E p DE processes the optimization procedures on the constructed classification LightGBM model. This enables an enhanced machine learning-based optimization technique that not only maintains the sufficiently accurate optimal design of structures but also significantly reduces the required computing efforts, as compared to standard optimization schemes. Various examples of steel structure designs (i.e., two of which have been provided herein) have been successfully performed by the proposed approach. These illustrate the accuracy and robustness of the proposed method, where good comparisons with reference algorithms (including standard DE with "DE/rand/1" mutational strategy, Jaya, Rao-1 and CaDE) are evidenced. The statistical values collected present the accuracy and reliability of the proposed method in obtaining the minimum total weight designs with substantial (some 60%) reduction of computing efforts required to furnish optimal results. [ABSTRACT FROM AUTHOR]

Stochastic Gradient Descent (SGD) is one of the most popular first-order methods to solve optimization problems in large-scale, which has also been widely studied in intelligence system, deep neural networks, or machine learning. In this paper we set out to revisit SGD with practical concerns in mind and hope to provide intuition on how SGD should be done in the right way in applications for expert and intelligence systems. In literature, when implementing SGD, regularization is often added to the loss function, to avoid ill-conditioning or overfitting, or to obtain solutions with desirable sparse structure. Intuitively, regularized loss function deviates from the true loss and in this case SGD may lead to a suboptimal solution. In this paper we revisit the matter of l 2 regularization for SGD, to investigate whether or when an explicit regularizer is necessary to obtain the desirable performance. We further introduce a simple stochastic algorithm (ASG) using the accumulated stochastic gradient of the un-regularized loss. Then experiments are carried out on benchmark data sets to validate the theoretical analysis. The findings show that for l 2 regularization, (1) SGD without explicit regularizing (SGDE) actually possesses an implicit regularizer, and in the sense of upper bound of the convergence rate, it outperforms SGD regularized explicitly (SGDER) with a constant advantage; (2) ASG without explicit regularizing outperforms both SGDE and SGDER, especially in the case where the number of iteration T cannot be pre-specified; (3) for SGD algorithms, the schemes that places flexible weight on the output of the latest iteration can give a better trade-off, compared with the scheme using the output of the last iteration or taking the average of the output of each iteration, and this suggests that in application a tunable averaging scheme is preferable. This study provides insights on using SGD algorithms for big data applications, e.g., to accelerate SVMs or regularized regression in large-scale, or to improve the performance of online learning or real time forecasting (control). In particular, when T is pre-specified, SGDE (without an explicit regularizer) can give us a well enough perforce with simplicity and understandability; when T cannot be pre-specified, ASG can improve the performance of standard SGD algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals' procedure of exploration and exploitation in AO-AOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter's (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works. [ABSTRACT FROM AUTHOR]

To adapt to the uncertainty of the real environment and solve the problem of high efficiency and high quality, this study comprehensively considers a variety of uncertainty factors, focusing on modeling and solving a hybrid flow-shop scheduling problem by two-stage stochastic programming (HFSP-TSP). First, this paper uses a two-stage scenario tree to describe the uncertain factors, the probabilities and values are obtained under different discrete production scenarios, and stochastic programming theory is used to formulate a mixed-integer linear programming (MILP) model for HFSP-TSP. Second, based on a general discrete optimization algorithm framework, that is, pointer-based discrete differential evolution (PDDE) algorithm, a novel variant (H-PDDE) is proposed for effectively solving the HFSP-TSP. It adopts a permutation scheduling decoding method and introduces three new improvement strategies: the strategy of inconsistent code length of individuals in the population, adding local search in discrete mutation operation, using a heuristic algorithm to generate the initial population. Finally, computational experiments were conducted to illustrate the proposed mathematical model and optimization algorithm. The computational results show that the H-PDDE can solve the HFSP-TSP more effectively than existing algorithms and conventional PDDE variants. The stochastic programming model is increasingly superior to the deterministic model in an uncertain environment. The source code of the H-PDDE algorithm and its data files can be found at https://github.com/huangyiping-ai/H-PDDE-algorithm.git , which is publicly available. [ABSTRACT FROM AUTHOR]

Sugarcane is one of the main commodities in the world economy. An important step in the sugarcane supply chain is the harvest stage, requiring the support of mathematical modeling and optimization tools. Integrating the process of choosing the areas for harvesting according to the sugarcane maturation with the formation of the harvesting fronts and associating them with machine relocation costs is challenging. This paper contributes with a novel integer mathematical model that considers these concerns using three conflicting objectives. The first is that the sugarcane should be harvested as close as possible to its maturity, considering the sugarcane should be harvested as close as possible to its maturity by considering a combined measure of temperature and time since planting. The second aims to minimize the number of harvest fronts. The third objective is to minimize the costs involved with the relocation of the harvesters that cut the cane. Different strategies for obtaining Pareto-optimal solutions were proposed using exact methods, including MIP start and a decomposition technique. In the worst case, solutions with tolerable integrality gaps were obtained (inferior to 3%) in less than 1.5 h. Computational tests in real case studies were conducted in the sugarcane industry in two countries, indicating strong trade-offs in the determined solutions. In the Brazilian case, harvesting as close to maturation as possible, for example, causes an increase of 158% in the number of harvest fronts and 47% in the transportation costs, considering the minimum values for these objectives. Other trade-offs were seen and discussed when one of the other objectives was prioritized. These different compromise solutions help the decision-makers choose the most appropriate harvesting schedule according to their preferences. • We propose a new MIP optimization model to determine the harvesting of sugarcane. • The model aims to optimize three conflicting objectives. • A new method of computing cane maturation is used: the concept of degree-days. • A procedure for determining Pareto solutions by exact methods is proposed. [ABSTRACT FROM AUTHOR]

With the penetration of Internet of things in manufacturing industry, it is an unavoidable issue to maintain robust wireless connections among machines and human workers in harsh industrial environments. However, the existing wireless planning tools focus on office environments, which are less harsh than industrial environments regarding shadowing effects of diverse obstacles. To fill this gap, this paper proposes an over-dimensioning (OD) model, which automates the decision making on deploying a robust industrial wireless local area network (IWLAN). This model creates two full coverage layers while minimizing the deployment cost, and guaranteeing a minimal separation distance between two access points (APs) to prevent APs that cover the same region from being simultaneously shadowed by an obstacle. Moreover, an empirical one-slope path loss model, which considers three-dimensional obstacle shadowing effects, is proposed for simple yet precise coverage calculation. To solve this OD model even at a large size, an efficient genetic algorithm based over-dimensioning (GAOD) algorithm is designed. Genetic operators, parallelism, and speedup measures are tailored to enable large-scale optimization. A greedy heuristic based over-dimensioning (GHOD) algorithm is further proposed, as a state-of-the-art heuristic benchmark algorithm. In small- and large-size OD problems based on industrial data, the GAOD was demonstrated to be 20%–25% more economical than benchmark algorithms for OD in the same environment. The effectiveness of GAOD was further experimentally validated with a real deployment system. Though this paper focuses on an IWLAN, the proposed GAOD can serve as a decision making tool for deploying other types of robust industrial wireless networks in terms of coverage, such as wireless sensor networks and radio-frequency identification (RFID) networks. [ABSTRACT FROM AUTHOR]

The least squares support vector machine (LSSVM) has achieved great success in various fields, but it still has certain limitations. Firstly, it treats all points equally and does not take into account the impact of different sample locations. Secondly, it is vulnerable to outliers and noise. Moreover, when class-imbalanced data are encountered, the decision boundary is biased toward the minority class. To address above problems, this paper introduces a bounded linear-exponential (BLINEX) loss function into LSSVM (LSKB), where the asymmetry and boundedness of the BLINEX loss function allow LSKB to assign distinct weights to each sample and be insensitive to noise and outliers. Further, this paper extends LSKB to a cost sensitive (CSLSKB) form to make it adapt to the class-imbalanced data. A fast optimization algorithm based on NESVM is developed to solve non-convex LSKB and CSLSKB (NEBLL). Numerous experiments demonstrate their effectiveness in dealing with class-balanced as well as class-imbalanced problems. • The paper complements the application of BLINEX loss function in machine learning. • LSKB inherits the benefits of both BLINEX and LSSVM. • CSLSKB empowers LSKB to cope with class-imbalanced problems. • A fast optimization algorithm is developed to solve LSKB and CSLSKB. • Numerous experiments demonstrate the effectiveness of LSKB and CSLSKB. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

• A novel SMO inspired by mimicking behavior to solve optimization problems is presented. • SMO includes a mimic operator to simulate search in the response space. • SMO does not require control operator respect to other Meta heuristic methods. • SMO solve optimization problems with minimum population size. • SMO is compared with 14 well-known and state of the art optimization algorithms. Increase in complexity of real world problems has provided an area to explore efficient methods to solve computer science problems. Meta-heuristic methods based on evolutionary computations and swarm intelligence are instances of techniques inspired by nature. This paper presents a novel social mimic optimization (SMO) algorithm inspired by mimicking behavior to solve optimization problems. The proposed algorithm is evaluated using 23 test functions. Obtained results are compared with 14 known optimization algorithms including Whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), Particle Swarm Optimization (PSO), Stochastic fractal search (SFS), Grey Wolf Optimizer (GWO), Optics Inspired Optimization (OIO), League Championship Algorithm (LCA), Wind Driven Optimization (WDO), Harmony search (HS), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Bat Algorithm (BA), and Teaching Learning Based Optimization (TLBO). Obtained results indicate higher capability of the SMO algorithm in solving high-dimensional decision variables. Furthermore, SMO is used to solve two classic engineering design problems. Three important features of SMO are simple implementation, solving optimization problems with minimum population size and not requiring control parameters. Results of various evaluations show superiority of the proposed method in finding the optimal solution with minimum function evaluations. This superiority is achieved based on reducing number of initial population. The proposed method can be applied to applications like automatic evolution of robotics, automatic control of machines and innovation of machines in finding better solutions with less cost. [ABSTRACT FROM AUTHOR]

• This paper proposes a boosted MFO for global search and kernel extreme learning machines. • Two strategies have been introduced into MFO for a more stable balance. • The extensive results on benchmark problems and real datasets have been performed. • A hybrid kernel extreme learning machine model is established for financial stress prediction. Image, graphical abstract Moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on the navigation routine of moths in the environment known as transverse orientation. For some complex optimization tasks, especially high dimensional and multimodal problems, the conventional MFO may face problems in the convergence trends or be trapped into the local and deceptive optima. Therefore, in this study, two strategies have been introduced into the conventional MFO to get a more stable sense of balance between the exploration and exploitation propensities. First, Gaussian mutation is employed to increase the population diversity of MFO. Then, a chaotic local search is applied to the flame updating process of MFO for better exploiting the locality of the solutions. The proposed CLSGMFO approach was compared against a wide range of well-known classical metaheuristic algorithms (MAs) and various advanced MAs using 23 classical benchmark functions. It was shown that the designed CLSGMFO can outperform most of the popular MAs in terms of solution quality and convergence speed. Moreover, based on CLSGMFO, a hybrid kernel extreme learning machine model, which is called CLSGMFO-KELM, is established to deal with financial stress prediction scenarios. To investigate the effectiveness of the CLSGMFO-KELM model, the proposed hybrid system was tested on two widely used financial datasets and compared against a broad array of popular classifiers. The results demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance. Accordingly, the proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction. [ABSTRACT FROM AUTHOR]

• HPSO-SSM is proposed based on original particle swarm optimization. • A new wrapper-based feature selection approach based on HPSO-SSM is proposed. • The logistic map sequence is used to enhance the diversity in the search process. • An innovative position update model is presented to improve the position quality. • Our method outperforms seventeen extremely competitive methods in terms of accuracy. The "curse of dimensionality" is one of the largest problems that influences the quality of the optimization process in most data mining, pattern recognition, and machine learning tasks. Using high-dimensional datasets to train a classification model may reduce the generalization performance of the learned model. In addition, high dimensionality of the dataset results in high computational and memory costs. Feature selection is an important data preprocessing approach in many practical application domains that are relevant to expert and intelligent systems. Feature selection aims at selecting a subset of informative and relevant features from an original feature dataset. Therefore, using a feature selection approach to process the original data prior to the learning process is essential for enhancing the performance on the classification task. In this paper, hybrid particle swarm optimization with a spiral-shaped mechanism (HPSO-SSM) is proposed for selecting the optimal feature subset for classification via a wrapper-based approach. In HPSO-SSM, we make three improvements: First, a logistic map sequence is used to enhance the diversity in the search process. Second, two new parameters are introduced into the original position update formula, which can effectively improve the position quality of the next generation. Finally, a spiral-shaped mechanism is adopted as a local search operator around the known optimal solution region. For a complete evaluation, the proposed HPSO-SSM method is compared with six state-of-the-art meta-heuristic optimization algorithms, ten well-known wrapper-based feature selection techniques, and six classic filter-based feature selection methods. Various assessment indicators are used to properly evaluate and compare the performances of these approaches on twenty classic benchmark classification datasets from the UCI machine learning repository. According to the experimental results and statistical tests, the developed methods effectively and efficiently improve the classification accuracy compared with other wrapper-based approaches and filter-based approaches. The results demonstrate the high performance of the HPSO-SSM method in searching the feasible feature space and selecting the most informative attributes for solving classification problems. Therefore, the HPSO-SSM method has broad application prospects as a new feature selection approach. [ABSTRACT FROM AUTHOR]

• Non-convex economic dispatch (NCED) problems have been solved. • Artificial cooperative search algorithm (ACS) is used to solve NCED. • The valve-point loading effects and transmission loss are considered in the problem. • The constraint-handling strategy is to generate and work with feasible solutions. • The results confirmed ACS's capability in solving NCED problems well and robustly. According to no free lunch (NFL) theorem, a metaheuristic optimization method is superior to other metaheuristic optimization methods when it has focused on specific class of optimization problems. Thus, this paper focuses on developing artificial cooperative search (ACS) optimization algorithm to solve economic dispatch (ED) problems more precisely with less complexity than other metaheuristic optimization methods. ACS is a recently developed two population search algorithm based on coevolution process with high probability of finding optimal solution in complex and non-convex optimization problems. This merit is provided by balancing exploration of the problem's search space and exploitation of better results through use of two advanced evolutionary operators and only one control parameter. The constraint handling strategy of the proposed method for solving economic power load dispatch problems is to generate and work with feasible solutions along all the optimization iterations without any mismatch between the total amount of electric power generation and electricity demand plus network transmission loss. Unlike the penalty method, this strategy is unaffected by parameter setting of applied optimization method that complicates its applicability for solving economic power load dispatch problems. The feasibility of ACS for solving ED problem is tested on different lossy non-convex test systems of various sizes and complexities. The practical aspects such as satisfaction of power demand constraint, generation limits of generators and value-point loading effect are undertaken to solve ED problem in medium to relatively large-scale electric power systems. Obtained results confirm the ACS's capability in converging to a better solution highly robust within the reasonable computational time in all independent trials; all these as compared with other optimization algorithms reported in the literature for solving lossy non-convex ED problems. The results are analyzed statistically in terms of solution quality and computational efficiency. The statistical analyses reveal that ACS is a potential method to solve economic power load dispatch problems as it provides higher quality solution in comparison with other optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper develops a novel nature-inspired metaheuristic technique named snow ablation optimizer (SAO) for numerical optimization and engineering design. The SAO algorithm mainly emulates the sublimation and melting behavior of snow to realize a tradeoff between exploitation and exploration in the solution space and discourage premature convergence. The competitiveness and effectiveness of SAO are validated utilizing 29 typical CEC2017 unconstrained benchmarks and 22 CEC2020 real-world constrained optimization issues which consist of 7 process synthesis and design issues and 15 mechanical engineering issues. Additionally, to further verify its strength, the developed SAO is applied to extract the core parameters in photovoltaic systems. The simulation outcomes have demonstrated that the developed SAO is a very promising technique that can yield better performance than other state-of-the-art rival methods. The source code of SAO is publicly available at https://github.com/denglingyun123/SAO-snow-ablation-optimizer. [ABSTRACT FROM AUTHOR]

This study constructs a reinforcement learning-based hybrid algorithm for Aquila Optimizer (AO) and improved Arithmetic Optimization Algorithm (IAOA). The point of the hybrid algorithm is that Q-learning can dynamically select the AO and the IAOA at different stages for different problems. In Arithmetic Optimization Algorithm (AOA), the mathematical optimization acceleration (MOA) function is restructured to balance global search and local exploitation, which can effectively stay away from the local optimum. Moreover, an improved reward function is modeled for Q-learning, which makes our hybrid algorithm more efficient and accurate. A set of benchmark functions and two engineering optimization problems are employed to test the performance of the proposed hybrid algorithm in this paper. Compared with other algorithms, the results show that the proposed hybrid algorithm has higher convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper provides a new approach to feature selection based on the concept of feature filters, so that feature selection is independent of the prediction model. Data fitting is stated as a single-objective optimization problem, where the objective function indicates the error of approximating the target vector as some function of given features. Linear dependence between features induces the multicollinearity problem and leads to instability of the model and redundancy of the feature set. This paper introduces a feature selection method based on quadratic programming. This approach takes into account the mutual dependence of the features and the target vector, and selects features according to relevance and similarity measures defined according to the specific problem. The main idea is to minimize mutual dependence and maximize approximation quality by varying a binary vector that indicates the presence of features. The selected model is less redundant and more stable. To evaluate the quality of the proposed feature selection method and compare it with others, we use several criteria to measure instability and redundancy. In our experiments, we compare the proposed approach with several other feature selection methods, and show that the quadratic programming approach gives superior results according to the criteria considered for the test and real data sets. [ABSTRACT FROM AUTHOR]

Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. [ABSTRACT FROM AUTHOR]

• The significant and impressive performance of the Chimp Optimization Algorithm (ChOA) has been a great inspiration for this paper to study on its multi-objective version called MOChOA. • A memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. • Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. The Multi-Objective Chimp Optimization Algorithm (MOChOA), a multi-objective variation of the recently proposed ChOA, is developed in this research to address multi-objective optimization issues in various engineering problems. The optimization community has recently been offered numerous evolutionary and meta -heuristic optimization strategies for tackling optimization challenges. When analyzing multi-objective optimization (MOO) problems, these approaches frequently produce poor solutions because they do not accurately estimate the Pareto optimal solutions or increase the distribution across all objectives. The significant and impressive performance of the ChOA has been a great inspiration for this paper to study on its multi-objective version called MOChOA. In this approach, a memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. According to results, MOChOA can provide competitive results and outperform well-known intelligent algorithms such as ANSGAIII, ARMOEA, dMOPSO, EFRRR, IBEA, SPEA2SDE, SPEAR, and tDEA. Alongside the remarkable performance of the proposed algorithm, its light-weight structure causes a fast execution. The proposed MOChOA can be applied in various multi-objective optimization application, including engineering, science, chemical processes, economics and logistics when appropriate decisions must be made in the face of competing objectives. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

The sustainability of many critical systems, such as water transmission networks or electrical grid, requires predictive maintenance strategies to prevent malfunction of components. These strategies typically use a troubleshooting model to suggest the components that are most beneficial to replace. This paper suggests a new dimension, which considers not only replacement costs and failure probabilities of components, but also adjacency of the components being replaced. We propose a model in which replacing adjacent components is often beneficial, because they can be replaced in a single replacement action. This helps minimizing costs known as overhead costs, which include the cost of sending a team to perform the replacement, the disruption to service during the replacement, and more. We propose several algorithms and AI techniques to suggest economical replacement methods. Evaluation on a real-world water transmission network shows that near-optimal solutions return a solution very fast, which is very close in terms of expected cost to the optimal solution. • The paper suggests a new approach to predictive maintenance that takes into account locations of system components. • Our paper is the first in the literature to explore this approach to predictive maintenance. • This new approach is formally defined as an NP-hard optimization problem. • Multiple optimization and AI techniques are proposed to handle the defined problem. • The algorithms are evaluated on a real waterline network. [ABSTRACT FROM AUTHOR]

Due to its practicality, hybrid flowshop scheduling problem (HFSP) with productivity objective has been extensively explored. However, studies on HFSP considering green objective in distributed production environment are quite limited. Moreover, the current manufacturing mode is gradually evolving toward distributed co-production mode. Thus, this paper investigated a distributed hybrid flowshop scheduling problem (DHFSP) with objectives of minimization the makespan and total energy consumption (T E C). To address this problem, this paper designed a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG) by integrating the merits of genetic operator and iterated greedy heuristic. In this MOHIG, firstly, one cooperative initialization strategy is proposed to boost initial solutions' quality based on the previous experience and rules. Secondly, one knowledge-based multi-objective local search method is invented to enhance the exploitation capability according to characteristics of problem. Thirdly, an energy-saving technique is developed to decrease the idle energy consumption of machine tools. Furthermore, the effectiveness of each improvement component of MOHIG is assessed by three common indicators. Finally, the proposed MOHIG algorithm is compared with other multi-objective optimization algorithms, including SPEA2, MOEA/D, and NSGAII. Experimental results indicate that the proposed MOHIG outperforms its compared algorithms in solving this problem. In addition, this research can better guide practical production in some certain environments. • Considering green scheduling in distributed hybrid flowshop environment. • Designing a new energy-saving strategy into this problem. • Proposing a Pareto-based multi-objective hybrid iterated greedy algorithm (MOHIG). • Evaluating performance of the proposed MOHIG by conducting comparison experiments. [ABSTRACT FROM AUTHOR]

• A soft-sensing method based on GK-ARFNN is proposed. • The GK clustering algorithm is used to develop the initial fuzzy rule base. • The recursive link is introduced into the FNN to improve dynamic mapping ability. • A hierarchical adaptive second-order optimization algorithm is developed. To address the issue of soft-sensing of effluent total phosphorus in wastewater treatment processes (WWTPs), a soft-sensing system based on an adaptive recursive fuzzy neural network with Gustafson-Kessel (GK) clustering and hierarchical adaptive second-order optimization algorithm (HAS) is proposed in this paper. In GK-ARFNN, first, the GK clustering algorithm was utilized to cluster the input–output dataset. Thus, the establishment of the initial fuzzy rule base and the determination of the parameter value of the fuzzy set membership function was realized. Then, the recursive layer was added into FNN to improve the dynamic mapping ability of the system. Finally, the HAS algorithm was developed based on the improved Levenberg-Marquardt (LM) optimization algorithm, and all the free parameters of the GK-ARFNN were adjusted online using HAS to improve the generalization capability and prediction accuracy of the soft-sensing system. In addition, the convergence of the proposed GK-ARFNN algorithm was also analyzed in this paper, which can ensure the effectiveness of the solutions to modelling issues for practical industrial processes. The simulation results demonstrate that the GK-ARFNN-based soft-sensing system introduced in this paper achieved satisfactory accuracy in the prediction of effluent total phosphorus in WWTPs. The source codes of GK-ARFNN and some competitors can be downloaded from https://github.com/hyitzhb/GK-ARFNN. [ABSTRACT FROM AUTHOR]

• Broad Learning System is used to predict the voltage stability index online. • The voltage stability index forecast model outperforms comparison models. • The importance of input features is explained by SHAP-XGBoost algorithm. • Characteristic approximate sensitivity correction voltage stability L-index. With the development of artificial intelligence technology, more advanced algorithms can be used to predict the static voltage stability index more quickly and accurately. However, when the predicted stability index is beyond the set safety range, how to make the static stability index quickly get back into the safety range has brought challenges to the development of the smart grid. In this paper, the Broad Learning System (BLS) is used to predict the static voltage stability index online, and the feasibility of applying BLS to the voltage stability index is analyzed. It is tested on the IEEE-14 bus system and verified on the IEEE-118 bus system. The results show that the BLS algorithm is superior to the machine model in RMSE and MAPE. At the same time, the prediction performance of the BLS model when N-1 fault occurs in the system is considered. In order to solve the problem of how to optimize the system to make the voltage stability index return to the safe operation index when the stability index exceeds the safe operation index, this paper proposes a correction control model based on SHAP-XGBoost-BLS, which is composed of SHAP-XGBoost and BLS models. SHAP-XGBoost-BLS can easily calculate the approximate value of the sensitivity of each characteristic to the voltage stability index under any operation mode, the sensitivity is used to correct the power system, which is verified on the IEEE-14 bus system. The results show that when the voltage stability index exceeds the safety index, the model can give the system optimization scheme so that the stability index can run in the safe range. [ABSTRACT FROM AUTHOR]

In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. • An efficient optimizer is developed to solve engineering optimization problem. • Optimal coordination among overcurrent relays in power distribution system is examined. • A sigmoid acceleration coefficient chaotic class topper optimization is developed. • Different types of distribution network are verified with the developed optimizer. • The simulation results for the chosen systems with the proposed algorithm are found better than existing results. [ABSTRACT FROM AUTHOR]

Highlights • Developing a new Spiking Neural P Grey Wolf Optimization System. • Novel strategies for solving non-determinism problem of Spiking Neural P systems. • Design a new mathematical model of the grey wolf optimization algorithm. • Control the copying spikes process among neurons is developed. • Consider the time control approach to avoid non-determinism inside neurons. Abstract Spiking neural P systems (SN P systems, in short) are the latest branch of membrane computing; inspired by the biological behavior of spiking neurons. They are considered true distributed and parallel systems; modeled to solve time consumption problem and presented the concept of parallelism usage in the computing field. This paper proposes novel strategies for solving non-determinism problem of SN P systems. The proposed algorithm relies on the parallelism feature to simulate the social hierarchy, tracking, encircling, and attacking behaviors in the grey wolf optimizer. It is modeled by collaboration between a set of SN P systems to get a feasible solution in polynomial time. Moreover, a new method named the power of signal is proposed to control the copying spikes process between neurons and differentiate between the arithmetic operations. Additionally, a time control approach is proposed to avoid non-determinism inside neurons that applied the determinism feature during firing rules. The theoretical and empirical experiments proved that the algorithm can successfully halt and in addition to the effectiveness of the proposed neural systems in getting an optimal solution in a reasonable time. As a result, this study is counted as a significant advancement in intelligent and optimization systems, whereas it has a direct impact on enhancing the performance of these systems and their applications. [ABSTRACT FROM AUTHOR]

Highlights • The proposed algorithm considers a double pair of evolutionary operators. • The proposed approach uses local consensus formulations with reactive responses. • The proposed method guarantees leaderless movement decisions. • The proposed algorithm outperforms several evolutionary methods. Abstract Evolutionary Computation (EC) algorithms have been proposed as stochastic methods to solve complex optimization problems. The design of EC methods typically involves the construction of empirical operators based on abstractions of animal behaviors or physical and biological phenomena. Through its operators, every EC approach proposes a particular solution to the exploration-exploitation balance which is currently considered an unsolved problem within EC literature. On the other hand, multi-agent systems have been utilized as intelligent, cooperative and self-organized structures where the synergy of simple rules creates complex interactions among agents. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO is based on typical processes present in multi-agent systems, such as local consensus formulations and reactive responses. These operations are conducted by using appropriate operators that are applied in each evolutionary stage. A traditional EC algorithm considers in its operation the application of every operator without examining its final impact in the searching process. In contrast to other EC techniques, the proposed method uses additional operators to avoid the undesirable effects produced by the over-exploitation or suboptimal exploration of conventional operations. In order to illustrate the performance and accuracy of the proposed NCCO approach, it is compared to several well-known, state-of-the-art algorithms over a set of benchmark functions and real-world design applications. The experimental results demonstrate that NCCO's performance is superior to the test algorithms. [ABSTRACT FROM AUTHOR]

Highlights • An approach for optimizing weighted matchings in wireless networks is proposed. • A distributed and asynchronous weighted matching algorithm is presented. • A tree-based efficient heuristic is provided for energy-efficient communication. • Time and message complexities are analyzed. • The effectiveness of proposed approach is shown by extensive experiments. Abstract Weighted graph matching is one of the most fundamental graph theoretical problems used in network design, especially in wireless network services such as radio resource allocation, physical layer security, energy-efficient partner selection and optimizing storage capacity. In this paper we present a distributed heuristic which provides nearly-optimal weighted matchings in polynomial time. We also propose an algorithm to decrease the number of transmitted messages to provide energy-efficient operation. Our approaches assume the asynchronous communication model and small messages having O (log (n)) bits. These assumptions directly fit the battery powered wireless networks such as wireless ad hoc and sensor networks. To the best of our knowledge, we propose the first distributed weighted matching algorithm which benefits from augmentation of augmenting paths having size larger than 3 for these networks. We also provide results of our simulations on synthetic geometric graphs to model wireless networks. Extensive simulations reveal that our algorithm improves the approximation performances of the other weighted matching algorithms and closes the gap between the approximation ratio and the optimum up to 32%. [ABSTRACT FROM AUTHOR]

Highlights • A heuristic search strategy was proposed for optimal learning group formation. • NSGA-II, a multi-objective optimization tool, was utilized for optimal grouping. • It was capable of enhancing inter-group homogeneity and Intra-group Heterogeneity. • Efficient and reliable fitness functions were defined to stop iterations. • Various characteristics of any data types and range of variations can be handled. Abstract In modern education systems, plenty of research suggests that clustering the learners into optimal learning groups based on their multiple characteristics is a determining effort in enhancing the effectiveness of collaborative learning. Although there have been several evidences on developing and implementing appropriate computational tools to handle classification processes in expert and intelligent systems, the effectiveness and accuracy of optimal grouping algorithms are still worth improving. For instance, the majority of grouping processes in collaborative learning environments is orchestrated through single-objective optimization algorithms, which need to be revisited due to some intrinsic limitations. In this paper, we propose a novel algorithm capable of properly addressing a variety of optimization problems in optimal learning group formation processes. To this end, a multi-objective version of Genetic Algorithms, i.e. Non-dominated Sorting Genetic Algorithm, NSGA-II, was successfully implemented and applied to improve the performance and accuracy of optimally formed learning groups. In contrast to the previous related works applying single-objective algorithms, the main advantage of our work is simultaneous satisfaction of multiple targets predefined for the formation of optimal learning groups, especially the inter-homogeneity and intra-heterogeneity of each learning group, which significantly enhance both effectiveness and accuracy of optimal grouping processes in the underlying intelligent systems. Challenging the proposed optimization algorithms, both single- and multi-objective optimizers, with a similar grouping problem, clearly proved that the single-objective optimization technique has limited control and sensitivity to the quality of individual groups. Contrary to single-objective optimization techniques, which are mainly governed by adjusting the quality of the groups altogether in average, the proposed multi-objective algorithm not only takes the average desirability of all formed groups into account but also precisely monitors the fitness of each group in a potential solution distinctively. The generality of the proposed algorithm makes it a suitable candidate not only to handle optimal grouping in learning environments but also to be competent enough for grouping problems in other domains as well. [ABSTRACT FROM AUTHOR]

Highlights • Introduction of agile team formation as an optimization problem. • Proposing expert finding models to retrieve candidates for agile teams. • Introduction of coverage, communication and optimality as quality measures of teams. • Proposing retrieval models to rank generalizing specialist (i.e. T-shaped) members. • Evaluation of proposed models on real datasets gathered from StackOverflow. Abstract Agile team formation is an important requirement of software companies. Since the members of an agile team should be generalizing specialists (i.e. T-shaped experts), we need members who are specialist in a specific topic and have general knowledge in other topics of the team. Selecting such members results in an ideal team which is flexible, high-performing and low-cost. In this paper, we define the problem of agile team formation in which given a set of required skills of an agile team, the ideal output is a set of low-cost candidates who can collectively cover the required skills while they can effectively communicate with each other. We propose two retrieval models to address this problem and then we introduce three evaluation measures for assessment. These measures are coverage, communication and optimality. Our experiments on two test collections extracted from StackOverflow demonstrate the efficiency of our proposed models in comparison with several strong baselines. [ABSTRACT FROM AUTHOR]

Highlights • Depending on clustering algorithm used, the results may change. • Based on Multi-Algorithm Voting, robustness of cluster solutions can be improved. • OMAV is developed that uses an optimization algorithm as integrative method. • OMAV is applied to the example of country clustering using GLOBE data. • Increased robustness and reduced subjectivity are demonstrated. Abstract Currently, the influence of a single statistical cluster algorithm on the results of clustering procedures represents a major threat to the objectivity in clustering. To exemplify this question, this paper refers to country clustering in cross-cultural research. In this field, previous research has determined differing numbers of clusters, depending on choices available for the clustering procedure, leading to a high number of inconsistent results. Hence, it is argued that the variety in cluster solutions induced by the choice of different statistical cluster algorithms should be reduced. To this end, this study builds on Multi-Algorithm Voting (MAV) procedure introduced by Bittmann and Gelbard (2007) and presents an advancement to the MAV method. Specifically, MAV procedure is refined for the analysis of larger data sets using the simulated annealing algorithm for optimization. The use of this Optimized MAV (OMAV) is then demonstrated for country clustering in cross-cultural research. Specifically, a set of 57 countries is divided into 12 clusters based on work-related values obtained from GLOBE database reported in House et al. (2004). Thus, results clearly show that the objectivity of clustering results can be significantly improved based on OMAV. Implications for expert and intelligent systems on the use of OMAV are discussed. Namely, OMAV represents a powerful tool supporting the decision-making process in cluster analysis reducing the number of subjective and arbitrary decisions. Taken together, this study contributes to existing literature by providing an integrative and robust method of country clustering using OMAV and by presenting country clusters applicable to various settings. [ABSTRACT FROM AUTHOR]

Highlights • DCDG-EA algorithm uses reference vector decomposition to solve MaOPs. • CDOS selects an appropriate operator to generate offspring. • CDIS strategy simultaneously considers the convergence and diversity of solutions. Abstract Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence–diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence–diversity based operator selection (CDOS) strategy and convergence–diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. [ABSTRACT FROM AUTHOR]

Highlights • Two binary variants of the butterfly optimization algorithm (BOA) are proposed. • Two transfer functions are used to map the continuous search space to discrete one. • Twenty one UCI datasets are utilized in the experiments. • A superior performance of the proposed binary variants is proved in the experiments. Abstract In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. [ABSTRACT FROM AUTHOR]

Highlights • The proposed method hides sensitive rules using electromagnetic field optimization. • Several sensitive association rules are hided simultaneously in the method. • The proposed method also has fewer lost rules than other well-known algorithms. • Two fitness functions are proposed to find the solution with minimum side effects. • The method is evaluated on both real-world and synthetic datasets. Abstract Privacy preserving data mining has been a major research subject in recent years. The most important goal of this area is to protect personal information and prevent disclosure of this information during the data mining process. There are various techniques in the field of privacy preserving data mining. One of these techniques is association rules mining. The main purpose of association rules mining is to hide sensitive association rules. So far, various algorithms have been presented to this field in order to reach the purpose of sensitive association rules hiding. Each algorithm has its own specific functions and methods. To hide sensitive association rules, this paper presents an electromagnetic field optimization algorithm (EFO4ARH). This algorithm utilizes the data distortion technique to hide the sensitive association rules. In this algorithm, two fitness functions are used to reach the solution with the least side effects. Also, in this algorithm, the runtime has been reduced. This algorithm consists of a technique for exiting from local optima point and moving toward global optimal points. The performance of the proposed algorithm is evaluated by doing experiments on both real-world and synthetic datasets. Compared to four reference algorithms, the proposed algorithm shows a reduction in the side effects and better preservation of data quality. The performance of EFO4ARH is tested by standard deviation and mean Friedman ranks of error for standard functions (CEC benchmarks). In addition, hiding experiments show that our proposed algorithm outperforms existing hiding algorithms. [ABSTRACT FROM AUTHOR]

Highlights • We propose a new joint neural model for entity recognition and relation extraction. • Our model does not rely on external NLP tools nor hand-crafted features. • Entities and relations within the same sentence are extracted simultaneously. • We experimentally evaluate extensively on four datasets (ACE04, CoNLL04, ADE, DREC). • Our model achieves state-of-the-art performance in different contexts and languages. Abstract State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them. [ABSTRACT FROM AUTHOR]

The Gravitational Search Algorithm (GSA) has excellent performance in solving various optimization problems. However, it has been demonstrated that GSA tends to trap into local optima and are easy to lose diversity in the late evolution process. In this paper, a new hybrid algorithm based on self-adaptive Gravitational Search Algorithm (GSA) and Differential Evolution (DE) is proposed for solving single objective optimization, named SGSADE. Firstly, a self-adaptive mechanism based on GSA is proposed for improving the convergence speed and balancing exploration and exploitation. Secondly, the diversity of the population is maintained in the evolution process by using crossover and mutation operation from DE. Besides, to improve the performance of the algorithm, a new perturbation based on Levy flight theory is embedded to enhance exploitation capacity. The simulated results of SGSADE on 2017 CEC benchmark functions show that the SGSADE outperforms the state-of-the-art variant algorithms of the GSA. [ABSTRACT FROM AUTHOR]

Gene clustering is a prerequisite in the analysis of microarray data where sets of co-expressed genes are clustered. In this paper, a multi-objective clonal selection optimization algorithm (MCSOA) is developed based on the immune system behavior for gene clustering purposes in which the number of clusters can vary in a predefined range. To achieve a reliable clustering outcome on various gene expression (GE) datasets, the most effective clustering validity indexes are incorporated and represented in terms of two conflicting objective functions. For the sake of fast convergence to the optimal solutions, a new population updating mechanism is iteratively applied to select the less-dominate solutions of the previous iteration. The proposed clustering technique is implemented on various publicly available microarray datasets. Comparing the results with those of the widely used gene clustering techniques confirms the superiority and efficacy of the proposed technique. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of elaborating travel itineraries considering the visitors´ profiles, travel distances and costs. Other parameters such as how much the tourists value the attractions offered are particularly taken into account, as well as their preferences regarding the order of visiting these attractions. This problem can be seen as a traveling salesman problem with profits that also considers priority prizes. It is described by an optimization model based on mixed integer programming, which aims to generate itineraries that maximize the total value of the attractions visited and minimize the total travel cost involved. We present results for sets of instances based on randomly generated and real data by applying mathematical programming techniques and a tailored tabu search algorithm. To obtain the input parameters of the problem for the real cases, some statistical techniques are used to analyze the data collected, such as multivariate correspondence analysis. The resulting solutions illustrate the potential application of the proposed approach to support planning and formatting decisions of travel itineraries. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of the grasshopper optimization algorithm (GOA) based on the opposition-based learning (OBL) strategy called OBLGOA for solving benchmark optimization functions and engineering problems. The proposed OBLGOA algorithm consists of two stages: the first stage generates an initial population and its opposite using the OBL strategy; and the second stage uses the OBL as an additional phase to update the GOA population in each iteration. However, the OBL is applied to only half of the solutions to reduce the time complexity. To investigate the performance of the proposed OBLGOA, six sets of experiment series are performed, and they include twenty-three benchmark functions and four engineering problems. The experiments revealed that the results of the proposed algorithm were superior to those of ten well-known algorithms in this domain. Eventually, the obtained results proved that the OBLGOA algorithm can provide competitive results for optimization engineering problems compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an improved version of the Grey Wolf Optimizer (GWO) is proposed to improve the exploration and the exploitation ability of the GWO algorithm. This improvement is performed through using the chaotic logistic map, the Opposition-Based Learning (OBL), the differential evolution(DE), and the disruption operator (DO). Where, the chaotic logistic map and the OBL are used to initialize the candidate solutions and these approaches avoid the drawbacks of the random population and increase the convergence of the algorithm. Then, the DE operators are combined with the GWO algorithm, in which, the DE operators work as a local search mechanism to improve the exploitation ability of the GWO through updating the population. Also, after updating the solutions by using a hybrid between the GWO and the DE, the DO is used to enhance the exploration ability, in which, the DO is used to maintain the diversity of the population. Therefore, the combinations with chaotic logistic map, OBL, DE, and DO, provide the GWO with tools to better balance between the exploration and the exploitation of the search space without affecting the computational time required for this task. The proposed algorithm, called COGWO2D, is compared with other seven algorithms through a set of experimental series that have been performed over two benchmark functions, the classical CEC2005, and the CEC2014. Also, the performance of the proposed algorithm to improve the classification of the galaxy images is evaluated, where it is used as a feature selection method. The aim of this experiment is to select the optimal subset of features from the extracted features of the galaxy images. The experimental results support the efficacy of the proposed approach to find the optimal solutions of the global optimization problem, as well as, increase the accuracy of the classification of the galaxy images. [ABSTRACT FROM AUTHOR]

The Max-Sum diversity and the Max-Min diversity are two well-known optimization models to capture the notion of selecting a subset of diverse points from a given set. The resolution of their associated optimization problems provides solutions of different structures, in both cases with desirable characteristics. They have been extensively studied and we can find many metaheuristic methodologies, such as Greedy Randomized Adaptive Search Procedure, Tabu Search, Iterated Greedy, Variable Neighborhood Search, and Genetic algorithms applied to them to obtain high quality solutions. In this paper we solve the bi-objective problem in which both models are simultaneously optimized. No previous effort has been devoted to study the “combined problem” from a multi-objective perspective. In particular, we adapt the mono-objective methodologies applied to this problem to the resolution of the bi-objective problem, obtaining approximations to its efficient front. An empirical comparison discloses the best alternative to tackle this NP -hard problem. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of a decision support system based on evolutionary multi-objective optimization for deploying sensors in an indoor localization system. Our methods aim to provide the human expert who works as the sensor resource manager with a full set of Pareto efficient solutions of the sensor placement problem. In our analysis, we use five scalar performance measures as objective functions derived from the covariance matrix of the estimation, namely the trace, determinant, maximum eigenvalue, ratio of maximum and minimum eigenvalues, and the uncertainty in a given direction. We run the multi-objective genetic algorithm to optimize these objectives and obtain the Pareto fronts. The paper includes a detailed explanation of every aspect of the system and an application of the proposed decision support system to an indoor infrared positioning system. Final results show the different placement alternatives according to the objectives and the trade-off between different accuracy performance measures can be clearly seen. This approach contributes to the current state-of-the art in the fact that we point out the problems of optimizing a single accuracy measure and propose using a decision support system that provides the resource manager with a full overview of the set of Pareto efficient solutions considering several accuracy metrics. Since the manager will know all the Pareto optimal solutions before deciding the final sensor placement scheme, this method provides more information than dealing with a single function of the weighted objectives. Additionally, we are able to use this system to optimize objectives obtained from fairly complex functions. On the contrary, recent works that are referenced in this paper need to simplify the localization process to obtain tractable problem formulations. [ABSTRACT FROM AUTHOR]

In system design, the best system designed under a simple experimental environment may not be suitable for application in real world if dramatic changes caused by uncertainties contained in the real world are considered. To deal with the problem caused by uncertainties, designers should try their best to get the most robust solution. The most robust solution can be obtained by constrained min–max optimization algorithms. In this paper, the scheme of generating escape vectors has been proposed to solve the problem of premature convergence of differential evolution. After applying the proposed scheme to the constrained min–max optimization algorithm, the performance of the algorithm could be greatly improved. To evaluate the performance of constrained min–max optimization algorithms, more complex test problems have also been proposed in this paper. Experimental results show that the improved constrained min–max optimization algorithm is able to achieve a quite satisfied success rate on all considered test problems under limited accuracy. [ABSTRACT FROM AUTHOR]