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This paper has proposed a new adaptive discrete swarm optimization (ADSO) for the video tracking framework. Each target object is first presented by a search window with four-dimensional features, which include 2D coordinates of the search window, its width and height. The image in the search window of a target object is extracted to calculate the HSV histograms, which are used to establish a feature model for the target object. Then the particles fly in a sub-search-space to find an optimal match of the target. If any occlusion or disappearance of the target object is detected, the particles will adaptively update their searching strategies in order to recapture the target. The experimental results demonstrate that the ADSO can out-perform the traditional PSO algorithm in the aspects of high accuracy rate and fast tracking and relocating speed. [ABSTRACT FROM AUTHOR]

Privacy preserving data mining is a new research field that aims to protect the private information and avoid the leakage of this information during the data mining process. One of the techniques in this field is the Privacy Preserving Association Rule Mining which aims to hide sensitive association rules. Many different algorithms with particular approaches have so far been developed to reach this purpose. In this paper, a new and efficient approach has been introduced which benefits from the cuckoo optimization algorithm for the sensitive association rules hiding (COA4ARH). In this method the act of hiding is performed using the distortion technique. Further in this study three fitness functions are defined which makes it possible to achieve a solution with the fewest side effects. Introducing an efficient immigration function in this approach has improved its ability to escape from any local optimum. The efficiency of proposed approach was evaluated by conducting some experiments on different databases. The results of the execution of the proposed algorithm and three of the previous algorithms on different databases indicate that this algorithm has superior performance compared to other algorithms. [ABSTRACT FROM AUTHOR]

Optimization methods have shown to be a very important approach for control engineers. They emulate the decision-making ability of a human expert to tune the control gains for a process or system with the formulation and solution of a mathematical optimization problem. In such formulation, evolutionary algorithms (EAs) have been widely used to obtain the control gains. Nevertheless a bad selection of the control gains through the optimization process can result in instability of the closed-loop control system such that the convergence and diversity in the EAs can be compromised. In this paper the PID control tuning for a planar parallel robot with a five-bar mechanism to follow a highly nonlinear trajectory is stated as an off-line nonlinear dynamic optimization problem (OLNLDOP). In order to promote individuals with a stable behavior in the closed-loop control system, a dynamic constraint and a method to handle such constraint is proposed into the OLNLDOP and into eight different variants of the differential evolution algorithm, respectively. Comparative analysis shows that the proposal finds suitable solutions for the OLNLDOP with a better convergence time. Laboratory testing with the optimum PID control gains on a real prototype validates the tuning optimization method. [ABSTRACT FROM AUTHOR]

In many real-world optimization problems, several conflicting objectives must be achieved and optimized simultaneously and the solutions are often required to satisfy certain restrictions or constraints. Moreover, in some applications, the numerical values of the objectives and constraints are obtained from computationally expensive simulations. Many multi-objective optimization algorithms for continuous optimization have been proposed in the literature and some have been incorporated or used in conjunction with expert and intelligent systems. However, relatively few of these multi-objective algorithms handle constraints, and even fewer, use surrogates to approximate the objective or constraint functions when these functions are computationally expensive. This paper proposes a surrogate-assisted evolution strategy (ES) that can be used for constrained multi-objective optimization of expensive black-box objective functions subject to expensive black-box inequality constraints. Such an algorithm can be incorporated into an intelligent system that finds approximate Pareto optimal solutions to simulation-based constrained multi-objective optimization problems in various applications including engineering design optimization, production management and manufacturing. The main idea in the proposed algorithm is to generate a large number of trial offspring in each generation and use the surrogates to predict the objective and constraint function values of these trial offspring. Then the algorithm performs an approximate non-dominated sort of the trial offspring based on the predicted objective and constraint function values, and then it selects the most promising offspring (those with the smallest predicted ranks from the non-dominated sort) to become the actual offspring for the current generation that will be evaluated using the expensive objective and constraint functions. The proposed method is implemented using cubic radial basis function (RBF) surrogate models to assist the ES. The resulting RBF-assisted ES is compared with the original ES and to NSGA-II on 20 test problems involving 2–15 decision variables, 2–5 objectives and up to 13 inequality constraints. These problems include well-known benchmark problems and application problems in manufacturing and robotics. The numerical results showed that the RBF-assisted ES generally outperformed the original ES and NSGA-II on the problems used when the computational budget is relatively limited. These results suggest that the proposed surrogate-assisted ES is promising for computationally expensive constrained multi-objective optimization. [ABSTRACT FROM AUTHOR]

To deal with large datasets, sampling can be used as a preprocessing step for clustering. In this paper, an hybrid sampling algorithm is proposed. It is density-based while managing distance concepts to ensure space coverage and fit cluster shapes. At each step a new item is added to the sample: it is chosen as the furthest from the representative in the most important group. A constraint on the hyper volume induced by the samples avoids over sampling in high density areas. The inner structure allows for internal optimization: only a few distances have to be computed. The algorithm behavior is investigated using synthetic and real-world data sets and compared to alternative approaches, at conceptual and empirical levels. The numerical experiments proved it is more parsimonious, faster and more accurate, according to the Rand Index, with both k-means and hierarchical clustering algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes (a set of ten production rules of a Lindenmayer System with memory), moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System (L-System) that is used to design the neural network’s architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems and simulating dynamical systems. [ABSTRACT FROM AUTHOR]

• A novel fractional grey Chebyshev polynomial Bernoulli model is proposed. • The structure of the NGBM(1,1,Tn) model is variable. • Comparative study shows that the new model is superior to other five grey models. Accurate mid-to-long term China's renewable energy forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, this paper proposes a novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model. The new model is based on NGBM(1,1) model to describe nonlinear phenomena. The proposed adjacent accumulation operator can balance old and new information accumulation with less fluctuation. The introduction of the Chebyshev polynomial enables the model to adaptively adjust the structure of the model, which reduces the difficulty of the modeler's operation from the point of view of the expert system. Meanwhile, an improved Grey Wolf optimization algorithm (IGWO) was used to optimize the parameters. Four real cases of renewable energy in China, including production and consumption are used for verification. In addition, Monte Carlo simulation and probability density analysis illustrate the robustness and accuracy of the proposed model. Finally, the development of the four cases in the next 5 years is predicted. [ABSTRACT FROM AUTHOR]

• Remora Whale Optimization Based-Hybrid deep model is used for detecting intrusions. • RWO is designed by the combination of ROA and WOA. • In the proposed approach features are selected by holoentropy process. • The developed approach achieves superior performance with testing accuracy of 0.93. Security remains as a key role in this internet world owing to the fast expansion of users on the internet. Numerous existing intrusion detection approaches were introduced by numerous researchers to recognize and identify intruders. Meanwhile, the existing systems failed to achieve satisfactory detection accuracy. Hence, this paper develops a robust intrusion detection model, named Remora Whale Optimization (RWO)-based Hybrid deep model for detecting intrusions. Here, the input data is pre-processed, and thereafter data transformation is done. With the transformed data, effective CNN features are extracted and feature conversion is performed to convert the features into vector form. Moreover, RV-coefficient is accomplished for performing feature selection process and finally, network intrusions are effectively detected using Hybrid deep model where the Deep Maxout Network and Deep Auto Encoder are used. On the other hand, the training procedure of the Hybrid deep model is carried out using the designed optimization algorithm, named RWO, which is the hybridization of the Remora Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). Furthermore, the devised technique achieved superior performance using the evaluation metrics, such as testing accuracy, precision, recall, and F1-score with the higher values of 0.938, 0.920, 0.932, and 0.926, respectively. [ABSTRACT FROM AUTHOR]

• Successful candidate strategy coupled with Runge-Kutta optimization (ScsRUN) is proposed. • Successful candidate strategy is added to boost global and local search mechanism. • A novel enhanced solution quality (ESQ) is adapted to break free from local positions. • ScsRUN is evaluated over 29 benchmark functions and a complex real world problem. • ScsRUN's superiority over advanced algorithms has been shown. The hydropower problems are non-linear and non-convex in nature; therefore, optimizing the operations of multi-hydropower plants in a multi-reservoir system is always challenging and complicated. In order to develop an appropriate optimization technique to address such difficulties, an efficient search strategy with a high capacity to move from exploration to exploitation in the feasible domain is required. This paper provides a successful candidate strategy combined with Runge-Kutta optimization (ScsRUN) to quickly, accurately, and reliably optimize multi-reservoir hydropower problems. Specifically, the successful candidate strategy is used to improve the stability between exploration and exploitation phases; a modified version of enhanced solution quality implemented in the original RUN (MESQ) is utilized to enhance the efficiency of solutions and break free from local positions; and sequential quadratic programming is performed as a robust local search method to accelerate convergence. The new optimization method developed in this study was evaluated by using 29 test functions and a real-world, complicated multi-reservoir problem. It was demonstrated that ScsRUN outperformed other advanced optimization methods in terms of efficiency and reliability. ScsRUN can be widely used to solve a variety of complicated problems. The last source codes of RUN algorithm is publicly available at https://imanahmadianfar.com. [ABSTRACT FROM AUTHOR]

• The proposed algorithm is effective for solving many-objective problems. • Shift-based estimation and sum of objectives can reserve the excellent solutions. • An angle-based selection strategy can improve the diversity of solutions. • Experimental results have been presented by using Wilcoxon's rank-sum test. Due to the curse of dimensionality, the existing evolutionary algorithms have difficulties in balancing convergence and diversity in many-objective problems. To address this shortcoming, this paper proposes an efficient many-objective optimizer named MaOEA-ASS. In the MaOEA-ASS, the angle-based selection strategy is used to obtain solutions with good diversity from the population. In addition, the combination of the shift-based density estimation and the sum of objectives, which uses the iteration information and emphasis the distribution of solutions, is employed to obtain the high-quality solutions approximating the optimal Pareto solutions. The proposed MaOEA-ASS is compared with eight state-of-the-art many-objective optimization algorithms (MaOEAs) on the DTLZ and WFG test suites, and its performance is verified on a practical many-objective problem. The experimental results demonstrate that the proposed MaOEA-ASS has a superior performance over the peer competitors on all considered many-objective problems. [ABSTRACT FROM AUTHOR]

• The path planning and path smoothing are combined through the high-order continuous Bezier curve. • An improved QPSO that incorporates two search mechanisms from bald eagle search (BES) is proposed. • The concept of semi-randomization is infused in some of our modifications in BES-MQPSO. • Enhanced intensive exploitation is performed by incorporating BES search and swoop mechanisms. • It may be the first time to apply BES tricks to help to enhance QPSO and into the path planning field. The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively. [ABSTRACT FROM AUTHOR]

This study considers a constrained huge-scale optimization problem over networks where the objective is to minimize the sum of nonsmooth local loss functions. To solve this problem, many optimization algorithms have been proposed by employing (sub)gradient descent methods to handle high-dimensional data, but the computation of the entire sub(gradient) becomes a computational bottleneck. To reduce the computational burden of each agent and preserve privacy of data in time-varying networks, we propose a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm over time-varying networks, in which the coordinates of the subgradient vector is randomly chosen to update the optimized parameter and the partially homomorphic cryptography is used to protect the privacy of data. Further, we prove that our algorithm is convergent asymptotically. Moreover, the rates of convergence are also established by choosing appropriate step sizes, i.e., O (log K / K) under local strong convexity and O (log K / K) under local convexity, in which K represents the number of iterations. Meanwhile, we show that the privacy of data can be protected by the proposed algorithm. The results of experiments demonstrate the computational benefit of our algorithm on two real-world datasets. The theoretical results are also verified by different experiments. • This paper proposes a privacy-preserving decentralized randomized block-coordinate subgradient projection algorithm. • It proves that the proposed algorithm is asymptotically convergent. • It proves that the proposed algorithm can protect the privacy of data. • It proves that the rate of convergence is achieved, i.e., O (log K / K) and O (log K / K). [ABSTRACT FROM AUTHOR]

In the paper, "A two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints" by Xiao-long Zheng and Ling Wang, published in a 2016 volume of Expert Systems With Applications , the authors present a mixed integer linear programming model (MILP) as a solution. This model is stated incorrectly, as it solves the problem of unrelated parallel machine scheduling without the additional resource constraint, which is not the given condition. A counterexample is provided in this note to demonstrate that the solution provided by the given MILP model not feasible for the considered problem. An adjustment on the MILP was developed to find an optimal solution for the problem addressed. The validation of the new MILP is verified. [ABSTRACT FROM AUTHOR]

• Improved variant of PO by making algorithmic and parametric changes is proposed. • IPO enhances exploration capability and balance of PO. • Performance evaluated on CEC-2014, CEC-BC-2017, and mechanical engineering problems. • Performance is compared with 25 meta-heuristics. Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation capability. However, it is found that PO prematurely converges for complex problems because of not giving enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a random member of a random party is incorporated in the party-switching phase. Moreover, the balance between exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical engineering problems. It is shown through non-parametric statistical Wilcoxon's rank-sum test that IPO significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared optimization algorithms. [ABSTRACT FROM AUTHOR]

• Ontology construction model under cross-domain application. • Proposed Jaccard Similarity Evaluation, Data filtering and Outlier Detection. • Semantic annotation, cluster, ontology construction with optimal centroid selection. • Hybrid algorithm of Whale Optimization Algorithm and Rider Optimization Algorithm. Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated similarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models. [ABSTRACT FROM AUTHOR]

• The symbiotic organisms search algorithm is investigated and corrected by removing the benefit factors. • Schemes for the mutualistic equations are suggested in order to prevent premature convergence. • Adopting lower combination rates for parasitic equations are recommended. The symbiotic organisms search (SOS) algorithm is investigated and corrected by removing the benefit factors due to their biased search around the original dot. However, the removal of these benefit factors results in performance that is far inferior to the outstanding performance of the basic SOS algorithm. Accordingly, this paper suggests adopting combination schemes for the mutualistic equations in order to prevent premature convergence, and further recommends adopting lower combination rates for parasitic equations. Combination schemes are found to be not applicable to the commensal equation, as this equation is not greedy. Therefore, this paper proposes two types of combination schemes to improve the corrected SOS version in terms of achieving high early convergence speed, attaining convergence precision at a lower cost, arriving at the convergence plateau at either a lower cost or a higher level of precision, handling tests of composition functions well, and achieving competitive performance on CEC2015 test problems. [ABSTRACT FROM AUTHOR]

In this paper, an improved Barnacles Mating Optimizer (BMO) is proposed to deal with optimization problems and develop a new automatic clustering approach. BMO is a well-established optimization technique inspired by the mating behavior of barnacles in real-life. The exploratory trends of BMO are influential and can maintain the right balance among exploration and exploitation. However, this population-based method can be improved further to reduce the probability of potential drawbacks for any optimization technique. As such, we revised the core searching phased of BMO based on a sine–cosine algorithm (SCA) and disruption operators (DO). The proposed method is named BMSCD, which updates the current solution by switching between the mechanisms of the BMO and SCA based on a probability calculated using the fitness value of the current solution. The experiments results on various benchmark cases for global optimizations demonstrate the improved performance of the proposed BMSCD in terms of quality of solutions, the balance of the exploration–exploitation, and convergence rates. Besides, the proposed BMSCD is evaluated by nine measures in solving different clustering problems. The results show that the BMSCD can effectively and powerfully address the tested problems and provide excellent performance compared to the state-of-the-art methods. • Developed an improved Sine–Cosine-Barnacles Algorithm Optimizer. • Evaluate the proposed method against benchmark functions and data clustering problems. • Compared the proposed method to other well-known methods. • Demonstrated effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A reliable landscape visualization is important in environmental management. Environmental managers need accurate spatially continuous data across an area to make competent and confident decisions. Spatial interpolation methods can be used to predict the height values at unsampled sites using data from point observations. In this paper, we propose three novel algorithms for spatial interpolation methods using kriging models. Since there are not many findings of how kriging parameters in the semivariance model affect the performance of the spatial interpolators, we explore different parameter selection methods of the kriging algorithm and propose three optimization algorithms (EPO, EKO, and ETL) to improve the performance of the spatial interpolation technique. Maximum likelihood estimation (MLE)-based parameter selection produces substantially less error than traditional semivariogram fitting. Our three models are compared against four MLE-based models. The performance is evaluated by error reduction that the seven models can perform. The strengths of each model are analyzed based on a different set of input sizes coming from two zones of study areas. The resulting errors of our proposed methods are relatively small. Comparing methods adopting the same exponential model, we observe that our recommended EKO performs marginally better than the exponential method based on MLE. In general, the result implies non-significant differences among models. Our work offers additional alternative parameter selection methods with equally high performance, and these methods can be practically used to improve the 3D surface plot in environmental management. [ABSTRACT FROM AUTHOR]

• A new distance domination relation is proposed to select the non-dominant solution. • The association criterion of NSGA- III algorithm is improved. • Combined with the proposed distance domination relation improved NSGA- III algorithm. There are two main aspects of research in many-objective optimization algorithm, namely, convergence and diversity. However, it is difficult for original algorithms to maintain the diversity of solutions in the high-dimensional objective space. The NSGA-III algorithm is an advanced algorithm based on Pareto dominance. In the high-dimensional objective space, the diversity maintenance of this algorithm is obviously lacking. In order to enhance the diversity of algorithms in many-objective optimization problems, a new distance dominance relation is proposed in this paper. First, in order to ensure the convergence of the algorithm, the distance dominance relation calculates the distance from the candidate solution to the ideal point as the fitness value, and selects the candidate solution with good fitness value as the non-dominant solution. Then, in order to enhance the diversity of the algorithm, the distance dominance relation sets each candidate solution to have the same niche and ensures that only one optimal solution is retained in the same territory. Finally, the NSGA- III algorithm is improved based on the proposed distance dominance relation. On the DTLZ and MaF test problems with 3, 5, 8, 10, and 15 objectives, the improved algorithm is compared with seven commonly used algorithms. The experimental results show that the improved algorithm is highly competitive and can significantly enhance the diversity of the algorithm. [ABSTRACT FROM AUTHOR]

• A search algorithm is investigated as an optimization method. • The proposed approach covers static, constrained, and dynamic problems. • The search algorithm is based on random solutions in all generations. • Proof of stochastic mechanisms of the method's core algorithm is provided. In this work, the application of an optimization algorithm is investigated to optimize static and dynamic engineering problems. The methodology of the approach is to generate random solutions and find a zone for the initial answer and keep reducing the zones. The generated solution in each loop is independent of the previous answer that creates a powerful method. Simplicity as its main advantage and the interlaced use of intensification and diversification mechanisms--to refine the solution and avoid local minima/maxima--enable the users to apply that for a variety of problems. The proposed approach has been validated by several previously solved examples in structural optimization and scored good results. The method is also employed for dynamic problems in vibration and control. A modification has also been done on the method for high-dimensional test functions (functions with very large search domains) to converge fast to the global minimum or maximum; simulated for several well-known benchmarks successfully. For validation, a number of 9 static and 4 dynamic constrained optimization benchmark applications and 32 benchmark test functions are solved and provided, 45 in total. All the codes of this work are available as supplementary material in the online version of the paper on the journal website. [ABSTRACT FROM AUTHOR]

Due to their faster convergence rate than gradient descent algorithms and less computational cost than second order algorithms, conjugate gradient (CG) algorithms have been widely used in machine learning. This paper considers conjugate gradient in the mini-batch setting. Concretely, we propose a stable adaptive stochastic conjugate gradient (SCG) algorithm via incorporating both the stochastic recursive gradient algorithm (SARAH) and second order information into the CG-type algorithm. Unlike most of existing CG algorithms that spend a lot of time in determining the step size by using line search and may fail in stochastic optimization, the proposed algorithms use a local quadratic model to estimate the step size sequence, but do not require computing the Hessian information, which make the proposed algorithms attain a low computational cost as first-order algorithms. We establish the linear convergence rate of a class of SCG algorithms, when the loss function is the strongly convex. Moreover, we show that the complexity of the proposed algorithm matches modern stochastic optimization algorithms. As a by-product, we develop a practical variant of the proposed algorithm by setting a stopping criterion for the number of inner loop iterations. Various numerical experiments on machine learning problems demonstrate the efficiency of the proposed algorithms. • The efficacy of conjugate gradient with noisy gradients is verified. • The linear convergence result of our method for strongly convex cases is obtained. • Our methods outperform several modern stochastic optimization methods. [ABSTRACT FROM AUTHOR]

• A new interdisciplinary metaheuristic derived from engineering field is proposed. • The new algorithm is developed based on the framework of the differential evolution. • Inspiration is derived from Proportional, Integral and Derivative control method. • CEC 2014 and CEC 11 functions are evaluated by the proposed optimizer. • CEC 2020 real-world constrained optimization problems are discussed. The proportional, integral, and derivative differential evolution algorithm (PID-DE) is proposed as a new type of interdisciplinary metaheuristic evolutionary algorithm in this paper. The inspiration of PID-DE is derived from the classical proportional, integral, and derivative control method in engineering, and it is used in the framework of the differential evolution (DE) algorithm. To begin, the mathematical models of proportional search, integral search, and derivative search are established as the fundamental search operations. Five different types of optimizers that use a combination of these three basic operations and an additional mutation operation are presented. The selection and crossover methods in DE are then modified to maintain population diversity while also improving global search capacity, and a feedback strategy is established to adaptively adjust the subgroup member of each optimizer. Following that, an integrated high-accuracy, rapid-convergence, and stable metaheuristic is invented using the comprehensive information utilization principle and flexible parameter determination method. Five groups of experiments are studied to assess the overall performance of the proposed algorithm. The first test comprises 12 standard benchmark functions with minimum optima. In Tests 2 and 3, the 52 functions of Congress on Evolutionary Computation (CEC) 2014 and CEC 11 are evaluated using PID-DE under standard test conditions. Besides, three classic real-world engineering design problems and the CEC 2020 test suit are studied for the constrained optimization test. The experimental tests validate PID-DE's higher accuracy and faster convergence speed in numerical optimization when compared with the representative approaches and top algorithms in the CEC competitions. [ABSTRACT FROM AUTHOR]

• Optimal foraging algorithm with direction prediction & Gaussian oscillation is built. • New algorithm includes two update methods: prediction model and random model. • Prediction model mends exploitation ability by predicting the evolutionary direction. • Random model mends exploration by exploring the space with Gaussian distribution. • The superiority of algorithm is verified on benchmark set and engineering problems. Optimal foraging algorithm (OFA) is a newly stochastic optimization technique and is famous for its computational accuracy. However, the high computational accuracy leads to slow convergence speed. Experimental results demonstrate that OFA is good at unimodal functions but poor at multimodal functions. To improve these drawbacks, in this paper a novel modified OFA with direction prediction and Gaussian oscillation, named OFA/P&G is introduced. In OFA/P&G, a transition matrix is constructed when a new global optimum is found to generate the candidate individuals. If the current global optimum does not change, the Gaussian oscillation is employed in a low probability and OFA update method is used in a high probability to generate the candidate individuals. The superior performance of OFA/P&G is verified on the 12 CEC2017 benchmark functions, 13 constrained benchmark functions and 5 engineering problems. Experimental results demonstrate that OFA/P&G outperforms other comparative algorithms. Finally, a real-world problem, drilling path optimization, is solved by OFA/P&G. [ABSTRACT FROM AUTHOR]

• The purpose of this study is to solve multi-modal multi-objective problems. • Using the adaptive parameter adjustment strategy to extend the search space. • The dynamic neighborhood forming strategy can exchange the information between particles in time. • The mutation operator is embedded to make the particle jump out of the local optimum. To solve the multi-modal multi-objective optimization problems which may have two or more Pareto-optimal solutions with the same fitness value, a new multi-objective particle swarm optimizer with a dynamic neighborhood balancing mechanism (DNB-MOPSO) is proposed in this paper. First, an adaptive parameter adjustment strategy is developed to balance the local and global search, which takes the difference among niches into consideration. Second, according to evolutionary states, a mutation operator is alternatively utilized to construct new solutions for escaping from the local optima. Then, combined with current niching methods, the dynamic neighborhood reform strategy of non-overlapping regions is properly implemented, which can enhance the exploration and keep the population diversity in the decision space. To validate the effectiveness of the proposed algorithm, DNB-MOPSO is compared with the other five popular multi-objective optimization algorithms. It is also applied to solve a real-world problem. The experimental results show the superiority of the proposed algorithm, especially in locating more optimal solutions in the decision space while obtaining the well-distributed Pareto fronts. [ABSTRACT FROM AUTHOR]

Moth–Flame Optimization (MFO) is widely utilized to solve optimization problems in different fields since it has a simple structure and easy implementation. However, MFO cannot effectively balance exploration and exploitation and often suffers from the lack of population diversity in the search process, especially in solving some complex engineering optimization problems. To overcome the above problems, in this paper, a multi-swarm improved moth–flame algorithm (MIMFO) is proposed. In MIMFO, firstly, the population is grouped and dynamically reorganized through chaotic grouping mechanism and dynamic regrouping mechanism, which can improve the grouping quality and diversity of the population. Secondly, spiral search and linear search are carried out for the two sub-swarms to improve the search efficiency and balance exploration and exploitation. In addition, Gaussian mutation is used to generate flame, which can accelerate convergence and enhance the exploration. The MIMFO is verified on 13 benchmark problems with 30, 500, 1000, 2000 dimensions and CEC 2014 test functions. The results show that the MIMFO is superior to other swarm intelligence algorithms and MFO variants in finding the global optimum and convergence performance. Finally, MIMFO is used to solve 57 engineering constraint optimization problems, and the results show that MIMFO can solve real-world engineering problems. • A multi-swarm improved moth–flame algorithm (MIMFO) is proposed. • Chaotic grouping and dynamic regrouping are used to improve population diversity. • Two sub-swarms are searched by spiral search and line search respectively. • Gaussian mutation is used to mutate the optimal flame. • 43 test problems and 57 engineering problems are used to evaluate the MIMFO. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) has been proven to be one of the most efficient metaheuristic algorithms in solving global optimization problems. However, it suffers from a slow convergence speed and premature convergence, especially when the complexity of the problem increases. To address these shortcomings, a ranking-based adaptive cuckoo search algorithm, called RACS, is proposed in this paper. Specifically, a novel ranking-based mutation strategy is designed at first, which is inspired by the natural phenomenon that good species or individuals always contain good information and thus have better odds of guiding others. In the proposed ranking-based mutation strategy, the global search equation is modified in combination with a ranking-based vector selection method, where some of the parent vectors are proportionally selected according to their rankings. The higher ranking a parent vector obtains, the more opportunity it will be chosen. Secondly, a crossover operation with parameter adaptation is employed after the Lévy flights random walk to preserve some good elements of the current solutions from being changed. Furthermore, a replacement strategy is designed to update the solutions not improved through pre-determined cycles by exploiting the beneficial information from the discarded solutions saved in the external archive. To evaluate the comprehensive performance of RACS, extensive experiments are conducted on three well-known test suites and an application problem of identifying unknown parameters of fractional-order nonlinear systems. Simulation results demonstrate that the presented strategies bring a significant improvement in effectiveness and efficiency on CS. Besides, RACS is verified to be superior or at least comparable to other CS variants and state-of-the-art algorithms on most of the benchmark problems, and thus, can be regarded as a useful and promising technique for solving real-world complex optimization problems. • A ranking-based adaptive cuckoo search algorithm is proposed. • A ranking-based mutation strategy is developed to enhance the exploitation ability. • The crossover operation is used to preserve some good elements from being changed. • A replacement strategy is designed to avoid stagnation. • RACS exhibits superior performance when solving various test problems. [ABSTRACT FROM AUTHOR]

• CM-RSOM merges robust designs and analyses for product and process improvements. • Interactive approach can be used to satisfy the decision maker's preferences. • EKO generates flexi-design plans to prevent designing new Taguchi arrays. • EKO provides various parameter estimations for the prediction model. • An industrial example demonstrates the utility of the proposed approach. This paper presents a novel centralised method to solve the manufacturing parameter design problem based on evolutionary computation elements using a novel elevator kinematic optimisation algorithm. The new method is referred to as compromise multi response surface optimisation method. Its evolutionary computation nature simultaneously produces several flexi-design plans to prevent a high number of experiments and is used within the design phase of Taguchi arrays. The new method also provides various channels via available information for choosing the parameter levels during the analysis phase without new individual fitting sets. The industrial application used for benchmarking the new approach revealed that the proposed design and analysis method could lead to better optimal solutions for complex processes such as resistance welding than those of a conventional Taguchi method. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to improve the optimization performance of gradient-based optimizers for training neural networks in metric learning. In metric learning, the neural networks use a linear activation in their output layer to generate real-valued data embeddings. Hence, the optimal model parameters can be deterministically calculated with respect to the output layer on the basis of the eigenvalue decomposition. These optimal model parameters that were computed by the eigenvalue decomposition were referred to eigen-guidance. The training performance of a gradient-based optimizer is boosted by replacing the current model parameters with the eigen-guidance at each pre-defined number of epochs. In addition, the optimal number of embedding dimensions is determined directly while computing the eigen-guidance without additional complexities. In our experiments, gradient-based optimizers with the eigen-guidance converged significantly faster than the gradient-based optimizers alone in metric learning tasks. • A new approach to accelerate gradient-based optimizers. • Deterministic update rule based on eigenvalue decomposition. • Generally applicable solver for embedding losses on Euclidean metric. • Accelerated convergence in the training of embedding networks. [ABSTRACT FROM AUTHOR]

To acquire high-quality resistivity inversion results, an intelligent resistivity inversion (IRI) framework based on a fuzzy wavelet neural network (FWNN) is proposed in this paper. In the IRI framework, first, the FWNN is applied to build an interpretable inversion model for analyzing the apparent resistivity data. Takagi–Sugeno–Kang (TSK) fuzzy model is introduced to FWNN to explain the rules of the inversion results, and the wavelet neural network (WNN) is applied to construct the consequent part for each fuzzy rule and enhance the high-order nonlinear fitting ability of TSK fuzzy model. Then, to enhance the generalization and robustness, an elastic gradient descent (EGD) method is designed, which is used to update the linear parameters of the FWNN. Next, a differential and adaptive whale optimization algorithm (DAWOA) is introduced to train the nonlinear parameters of the FWNN for avoiding local optimum. Moreover, in the proposed DAWOA, a differential foraging strategy and an adaptive predation strategy are introduced to improve global exploration and local exploitation. All these measures can improve the inversion accuracy and accelerate the training process of FWNN for ERI inversion. Several simulation results demonstrate the feasibility and applicability of the IRI framework. • FWNN is an interpretable inversion model for ERI. • WNN is applied to enhance the high-order nonlinear fitting ability. • EGD method is used to update the linear parameters. • DAWOA is introduced to train the nonlinear parameters. [ABSTRACT FROM AUTHOR]

• A velocity-based butterfly optimization algorithm is proposed. • A modified position updated equation is designed. • The refraction-based learning is introduced to enhance the diversity. • The extensive results show the competitiveness of the proposed algorithm. Throughout the last decade, high-dimensional function optimization problems have received substantial research interest in the field of intelligence computation. Butterfly optimization algorithm is a new meta-heuristic technique that has been proven to be more competitive than other optimization methods on low- dimensional problems. The basic butterfly optimization algorithm and its variants are not used to solve high-dimensional problems. Therefore, this paper develops an improved version of butterfly optimization algorithm to deal with the high- dimensional optimization problems. Inspired by particle swarm optimization, a modified position updated equation is designed by introducing velocity item and memory item in the local search phase to guide the search for candidate individuals. Moreover, a novel refraction-based learning strategy is introduced into butterfly optimization algorithm to effectively enhance diversity and exploration. The effectiveness of the proposed algorithm is tested on 40 benchmark problems with 100, 1000, 10,000 dimensions. The statistical results show that our algorithm has better performance than the basic butterfly optimization algorithm, its variants, and other population-based approaches to deal with the high-dimensional optimization problems. Finally, the high-dimensional feature selection and fault identification of wind turbine problems are solved and the comparisons show that the proposed algorithm outperforms better than most methods in terms of classification accuracy and number of the optimal feature subset. [ABSTRACT FROM AUTHOR]

• A novel hybrid ensemble forecasting frame is proposed based on many predictors. • Sub-model selection scheme is employed to select optimal predictors for sub-series. • Multi-objective optimization algorithm is used to get integrated forecast results. • Interval forecasting is conducted to further capture the uncertainty. Short-Term Electricity Load Forecasting (STLF) has drawn increasing attention due to its considerable function on improving the dispatch and management of power system. The path of load forecasting has been widely explored in the past studies. However, most forecasting methods ignore the importance of sub-model selection and interval forecasting, causing poor forecasting performance. In this paper, a hybrid ensemble forecasting scheme, constructed by sub-model selection, data decomposition, multi-objective optimization, point prediction, and interval prediction, is proposed to enrich the current load forecasting system. In the load forecasting system design, the original load data is adaptively decomposed into some sub-series by using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). The most proper sub-model for decomposed series is selected from nine widely-used forecasting methods based on sub-model selection. The forecasting results of each sub-series are ensembled by the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) to estimate the final point forecasting values. By integrating point forecasting errors and distribution fitting strategy, the interval forecasting results are further estimated, which investigates the effectiveness of the proposed system. Three numerical experiments are carried out based on the real load data obtained from three observation sites. The experimental results indicate that the hybrid ensemble system can provide better point and interval forecasting performance with the average Mean Absolute Percentage Error (MAPE) values of 0.3275%, 0.4419%, and 0.7024% and the mean Coverage Width-based Criteria (CWC) values (α = 0.05) of 0.0668, 0.1335, and 0.3178 from one-step to three-step forecasting, respectively. [ABSTRACT FROM AUTHOR]

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applications. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori , due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorporated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark example in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques. [ABSTRACT FROM AUTHOR]

This paper addresses inventory problem for the products that are sold in monopolistic and captive markets experiencing hybrid backorder (i.e., fixed backorder and time-weighted backorder). The problem with stochastic demand is studied first by developing single objective (cost) inventory model. Computational results of a numerical problem show the effectiveness of hybrid backorder inventory model over fixed backorder inventory model. The model is later extended to multi-objective inventory model. Three objectives of multi-objective inventory model are the minimization of total cost, minimization of stockout units and minimization of the frequency of stockout. A multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the inventory model and generate Pareto curves. The Pareto curves obtained for hybrid backorder inventory model are compared with the existing Pareto curves that are based on fixed backorder. The results show a substantial reduction in stockout units and frequency of stockout with a marginal rise in cost with proposed hybrid backorder inventory system in comparison to existing fixed backorder inventory system. Sensitivity analysis is done to study the robustness of total cost, order quantity, and safety stock factor with the change in holding cost. In the end, the performance of the MOPSO algorithm is compared with the multi-objective genetic algorithm (MOGA). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. [ABSTRACT FROM AUTHOR]

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html . [ABSTRACT FROM AUTHOR]

Design space exploration and metamodeling techniques have gained rapid dominance in complex engineering design problems. It is observed that the modeling efficiency and accuracy are directly associated with the design space. In order to reduce the complexity of the design space and improve modeling accuracy, a multi-stage design space reduction and metamodeling optimization methodology based on self-organizing maps and fuzzy clustering is proposed in this paper. By using the proposed three-stage optimization approach, the design space is systematically reduced to a relatively small promising region. Self-organizing maps are introduced to act as the preliminary reduction approach through analyzing the underlying mapping relations between design variables and system responses within the original samples. GK (Gustafson & Kessel) clustering algorithm is employed to determine the proper number of clusters by utilizing clustering validity indices, and sample points are clustered using fuzzy c-means (FCM) clustering method with the known number of clusters, so that the search can focus on the most promising area and be better supported by the constructed kriging metamodel. Empirical studies on benchmark problems with multi-humps and two practical nonlinear engineering design problems show the accurate results can be obtained within the reduced design space, which improve the overall efficiency significantly. [ABSTRACT FROM AUTHOR]

This paper presents the modified Firefly Algorithm (FA) originally proposed by Yang. Firefly Algorithm is based on the idealized behavior of the flashing characteristics of the fireflies. Though firefly is powerful in local search, it does not search well globally due to being trapped in local optimum. Due to this reason, the convergence is generally slow. The FA also doesn't give efficient solution in high dimensional problems. The proposed approach gives more efficient solution with reduced time complexity in comparison to original FA. Two modifications made are: (1) Opposition-based methodology is deployed where initialization of candidate solutions is done using opposition based learning to improve convergence rate of original FA, which includes initializing the opposite number of positions of each firefly. This also ensures efficient searching of the whole search space, (2) The dimensional-based approach is employed in which the position of each firefly is updated along different dimensions. This results in more optimal solution. This algorithm works for High Dimensionality problems, especially in terms of accuracy in finding the best optimal solution and in terms of fast convergence speed as well. Several complex multidimensional standard functions are employed for experimental verification. Experimental results include comparison with other Evolutionary algorithms which show that the Opposition and Dimensional based FA (ODFA) gives more accurate optimal solution with high convergence speed than the original FA and those achieved by existing methods. [ABSTRACT FROM AUTHOR]

Many real-world problems can be categorized as constrained optimization problems. So, designing effective algorithms for constrained optimization problems become more and more important. In designing algorithms, how to guide the individuals moving more efficiently towards the feasible region is one of the most important aspects on finding the optimum of constrained optimization problems. In this paper, we propose an improved ε constrained differential evolution, which combines with pre-estimated comparison gradient based approximation. The proposed algorithm uses gradient matrix to determine whether the trail vector generated by differential evolution algorithm is worth using the fitness function to evaluate it or not. Pre-estimated comparison gradient based approximation is used as a detector to find the promising offspring and in this way can we guide the individuals moving towards the feasible region. The proposed method is tested both on twenty-four benchmark functions and four well-known engineering optimization problems. Experimental results show that the proposed algorithm is highly competitive in comparing with other state-of-the-art algorithms. The proposed algorithm offers higher accuracy in engineering optimization problems for constrained optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, the concept of multi-objective optimization is introduced in the automation of the placement task in analog integrated circuits layout design. To bridge the difficulties found on state-of-the-art works on fulfilling proximity constraints, here, cells are organized into proximity groups which implement the desired set of symmetry and proximity requirements. Then, an innovative archived-based multi-objective simulated annealing algorithm, operating over an absolute representation, is proposed to optimize the placement of each proximity group. In contrast to traditional single-objective placement approaches, the resulting Pareto fronts of placements, representing the tradeoffs between the optimization objectives of each group, are combined, bottom-up, through the design hierarchy, until a final front is obtained. This way, the problem's complexity is reduced, and split over multiple executions of the optimization kernel with less design variables, and also, analog designer becomes aware of the design tradeoffs. The proposed multi-objective and hierarchical methodology was implemented, and, experimental results prove that previous efforts on single-objective absolute representations are no match for the obtained floorplans. Furthermore, the obtained Pareto fronts contain the solutions found with the most recent published topological representations for the well-known Microelectronics Center of North Carolina benchmark sets, and, allowed an improvement of placement area up to 23% on a previously optimized folded cascode operational amplifier for the United Microelectronics Corporation 0.13 μm fabrication process. [ABSTRACT FROM AUTHOR]

Disasters can lead to a high risk of casualties and structural damages. Destructive disasters, such as earthquakes, may cause a great amount of disaster waste to be controlled. Reusing and recycling materials in the debris can decrease the need for re-construction resources. These reusable or recyclable materials can be processed in temporary storage sites like the ones suggested by the United Nations and the United States Federal Emergency Management Agency guidelines on disaster waste management. The objective of this paper is to build a framework for determining the locations of temporary storage facilities, and includes planning for the collection and transportation of disaster waste in order to manage it in an environmentally sustainable way. In this study, a multi-objective optimization model is developed and solved with an evolutionary elitist multi-objective optimization algorithm (NSGA-II). As a city prone to high earthquake damage, Istanbul has been selected for the illustration of the proposed framework. The objectives of the model are cost minimization and minimization of risk from hazardous waste exposure. The study integrates disaster loss estimation methods with post-disaster waste management. [ABSTRACT FROM AUTHOR]

In the quest for greater autonomy, there is an increasing need for solutions that would enable a large set of robots to coalesce and perform complicated multi-robot tasks. This problem, also known as the multi-robot coalition formation problem has been traditionally approached as a single objective optimization problem. However, robots in the real world have to optimize multiple conflicting criteria such as battery life, number of completed tasks, and distance traveled. Researchers have only recently addressed the robot coalition formation problem as a multi-objective optimization problem, however the proposed solutions have computational bottlenecks that make them unsuitable for real time robotic applications. In this paper we address the issue of scalability by proposing parallelized algorithms in the CUDA programming framework. NSGA-II and PAES algorithm have been parallelized due to their suitability to the coalition formation domain as outlined in our previous work. The parallelized versions of these algorithms have been applied to both the additive and non-additive coalition formation environments. Simulations have been performed in the player/stage environment to validate the applicability of our approach to real robot situations. Results establish that the multi-point PAES parallel variant yields significant performance gains in terms of running time and solution quality when the problem is scaled to deal with large inputs. This suggests that the algorithm may be viable for real time robotic applications. Experiments demonstrate significant speedup when the proposed parallel algorithms were compared with the serial solutions proposed earlier. [ABSTRACT FROM AUTHOR]

While the auxiliary services required for the operation of power plants are not the main components of the plant, their energy consumption is often significant, and it can be reduced by implementing a series of improvement strategies. However, the cost of implementing these changes can be very high, and has to be evaluated. Indeed, a further economic analysis should be considered in order to maximize the profitability of the investment. In this paper, we propose a multiobjective optimization problem to determine the most suitable strategies to maximize the energy saving, to minimize the economic investment and to maximize the Internal Rate of Return of the investment. Solving this real-life multiobjective optimization problem with a decision maker presents several challenges and difficulties and we have developed a novel interactive procedure which combines three different approaches in order to make use of the main advantages of each method. The idea is to start with the approximation of the Pareto optimal set, in order to gain a global understanding of the trade-offs among the objectives, using evolutionary multiobjective optimization; next step is aiding the decision maker to explore the efficient set and to identify the subset of solutions which fits her/his preferences, for which interactive multiple criteria decision making methodologies are used; and finally we concentrate the search for new solutions into the most interesting part of the efficient set with the help of a preference-based evolutionary algorithm. This allows us to build a flexible scheme that is progressively adapted to the decision maker’s reactions until (s)he finds the most preferred solution. The interactive combined procedure proposed is applied in practice for solving the problem of the auxiliary services with a real decision maker, extracting interesting insights about the efficiency improvement of the auxiliary services. With this practical application, we show the usefulness of the interactive procedure proposed, and we highlight the importance of an understandable feedback and an adaptive process. [ABSTRACT FROM AUTHOR]

Facial landmarks can be used as a compact and effective representation of facial geometry. Although different facial landmark models (LMs) have been used for various face image analysis tasks, there are few works investigating the design of the LMs, which takes care of how many landmarks to use to represent facial geometry and where to locate them. Existing LMs are either not sufficient to approximate face shapes precisely or including many redundant landmarks. This paper aims to develop an optimized LM which can delicately represent facial geometry while keeping as few landmarks as possible. We divide the landmarks into two categories, namely key points (KPs) and inserted points (IPs). KPs include anatomical and mathematical landmarks which are defined with consideration of an anthropometry face model; IPs are pseudo-landmarks that help describe the face shape accurately. An iterative searching scheme is proposed to find a series of optimal positions for IPs until some acceptable error is obtained. The obtained LM includes 54 KPs and 44 IPs distributed on different facial regions. We compare our LM and other LMs with similar number of points in terms of shape approximation error and landmark detection error. Experimental results show the superiority of our LM in both criteria. The proposed LM is useful for applications that require delicate shape representations and promotes standardization of facial LMs. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of the Data Quality Query System (DQ 2 S), a query processing framework and tool incorporating data quality profiling functionality in the processing of queries involving quality-aware query language extensions. DQ 2 S supports the combination of performance and quality-oriented query optimizations, and a query processing platform that enables advanced data profiling queries to be formulated based on well established query language constructs, often used to interact with relational database management systems. DQ 2 S encompasses a declarative query language and a data model that provides users with the capability to express constraints on the quality of query results as well as query quality-related information; a set of algebraic operators for manipulating data quality-related information, and optimization heuristics. The proposed query language and algebra represent seamless extensions to SQL and relational database engines, respectively. The constructs of the proposed data model are implemented at the user’s view level and are internally mapped into relational model constructs. The quality-aware extensions and features are extremely useful when users need to assess the quality of relational data sets and define quality constraints for acceptable data prior to using candidate data sources in decision support systems and conducting big data analytical tasks. [ABSTRACT FROM AUTHOR]

Over decades, a large number of research studies have concentrated on improving the accuracy of classification model. This is the case as several types of classifiers prove to be useful in real-life problems, including the prediction of system failure risk and microarray-based cancer diagnosis. Despite this, the accuracy of existing classifiers has been constrained by uninformative variables typically observed in modern data. In addition to feature selection, one may transform the original data to another variation, where only key feature components are included. Unlike conventional transformation-based techniques found in the literature, this paper presents a novel method that makes use of cluster ensembles, specifically the summarized information matrix, as the transformed data for the following classification step. Among different state-of-the-art methods, the link-based cluster ensemble approach (LCE) provides a highly accurate clustering, and thus particularly employed here. This is uniquely coupled with a diversity-driven generation of ensemble, which provides informative and diverse sets of clusterings. The performance of this transformation model is evaluated on published synthetic, standard and gene expression datasets; using C4.5, Naive Bayes, KNN, Neural Network and Random Forest classifiers; in comparison with benchmark techniques. The findings suggest that the new model can improve the classification accuracy of original data and performs better than the other transformation methods investigated in the empirical study. [ABSTRACT FROM AUTHOR]

Recently, an improved ( μ + λ ) constrainted differential evolution (ICDE) has been proposed and proven to be robust and effective for solving constrainted optimization problems. However, so far, the ICDE has been developed mainly for continuous design variables, and hence it becomes inappropriate for solving layout truss optimization problems which contain both discrete and continuous variables. This paper hence fills this gap by proposing a novel discrete variables handling technique and integrating it into original ICDE to give a so-called Discrete-ICDE (D-ICDE) for solving layout truss optimization problems. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress, displacement and buckling limitations. Numerical examples of five classical truss problems are carried out and compared to other state-of-the-art optimization methods to illustrate the reliability and effectiveness of the proposed method. The D-ICDE’s performance shows that it not only successfully handles discrete variables but also significantly improves the convergence of layout truss optimization problem. The D-ICDE is promising to extend for determining the optimal solution of other structural optimization problems which contain both discrete and continuous variables. [ABSTRACT FROM AUTHOR]

Multimedia transmission over Mobile Ad-hoc Networks (MANETs) is crucial to many applications. However, MANETs possess several challenges including transmitting large size packets, minimizing delay, loss-tolerant and buffer size estimation. For effective multimedia transmission, delay should be minimized and packets should be received in the defined order. The existing standards such as 802.11b and 802.11e perform well in wireless networks, but exhibit poor response in MANETs for multimedia traffic, especially in multi-hop networks. In this paper, we first establish the dependency of delay on buffer size and packet size, and then present a delay optimization approach for multimedia traffic in MANETs. We use Knapsack algorithm for buffer management to maximize the in-order packets and minimize the out-of-order packets simultaneously. Our approach exploits the buffer internals and dynamically adjusts the buffer usage so that a node transmits the packets in the desired order to its successive nodes. Careful estimation of packet size and buffer size helps in minimizing the delay, improving the capability of receiving packets in the correct order and reducing out-of-order packets in the buffer at intermediate nodes. Our approach also controls the loss of multimedia data packets during transmission. We validate our approach with real-world examples using network simulator. [ABSTRACT FROM AUTHOR]

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance. [ABSTRACT FROM AUTHOR]

A novel bat algorithm (NBA) is proposed for optimization in this paper, which focuses on further mimicking the bats’ behaviors and improving bat algorithm (BA) in view of biology. The proposed algorithm incorporates the bats’ habitat selection and their self-adaptive compensation for Doppler effect in echoes into the basic BA. The bats’ habitat selection is modeled as the selection between their quantum behaviors and mechanical behaviors. Having considered the bats’ self-adaptive compensation for Doppler effect in echoes and the individual’s difference in the compensation rate, the echolocation characteristics of bats can be further simulated in NBA. A self-adaptive local search strategy is also embedded into NBA. Simulations and comparisons based on twenty benchmark problems and four real-world engineering designs demonstrate the effectiveness, efficiency and stability of NBA compared with the basic BA and some well-known algorithms, and suggest that to improve algorithm based on biological basis should be very efficient. Further research topics are also discussed. [ABSTRACT FROM AUTHOR]

The Blocks Relocation Problem seeks to find the shortest sequence of movements to retrieve a set of homogeneous blocks placed in a two-dimensional storage according to a predefined order. In this paper we analyze an optimization model recently published in the literature. We illustrate that this model reports infeasible solutions and does not guarantee the optimality of the achieved solutions in some cases. In order to overcome this fact, we propose an alternative optimization model. However, its high computational consumption of temporal and space resources in large environments encourages us to also develop a branch and bound algorithm to solve realistic scenarios to optimality. This algorithm includes an intelligent strategy to explore the most promising nodes in the underlying tree. Additionally, its performance can be easily adapted to report high-quality solutions at the expense of sacrificing the optimality guarantee. In contrast to previous exact proposals, the computational experiments reveal the high efficiency of the branch and bound algorithm, reporting optimal solutions for the most widely extended benchmark suite from the related literature through short computational times. [ABSTRACT FROM AUTHOR]

[Display omitted] • An literature review on IPO, IIR system identification, and combining the two. • Introducing an state-of-the-art version called IIPO. • Evaluation of IIPO and SIPO versions for the first time to design IIR filters. • Successful performance of the proposed methods via a proper fitness and two controls. • Proving the efficiency, stability, and reliability by 100 independent trials. The Inclined Planes System Optimization (IPO) algorithm is recent algorithm that uses Newton's second law to perform optimization. After conducting a thorough literature review, this paper proposes an improved version of IPO called IIPO. This improvement is achieved by changing exploratory and exploitative behavior of the standard IPO proportional to the progress of optimization (iteration). The IIPO is employed for optimizing IIR digital filter design, which is a challenging engineering problem. Adaptive IIR modeling as a multimodal optimization problem is designed and developed under system identification structure with an appropriate single-objective function in the frequency domain. Implementations are performed in both modeling cases with same and reduced orders, and under two identification forms with and without environmental additive noise. The results are reported along with various analyzes compared to a wide range of IPO variants. The statistical results on 100 independent trials show a success of more than 90% of cases, the proposed IIPO algorithm substantially outperforms other comparative algorithms in terms of accuracy of estimated coefficients, convergence, fitness, output responses, noise analysis, stability, and reliability. 1 1 All source codes are fully and publicly available at https://github.com/ali-ece. [ABSTRACT FROM AUTHOR]