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Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex dataset. To address these problems, a novel data adaptive Bregman clustering algorithm (DBGSA) is proposed in this paper, which combines the universal gravitational algorithm to bring similar points closer in the dataset. In addition, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8% compared to the contrasted algorithms, making it applicable to diverse datasets. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved version of Twin SVM using a non-smooth optimization method. Twin SVM generally consists in determining two non-parallel planes by alternately solving two constrained optimization models. Solving this problem using the classical Lagrangian method has many limitations, notably: its only limited to handle Gaussian noise, generally exaggerates the influence of outliers and cannot handle unbalanced data, this due to the differentiability of the model. To circumvent these issues, we transform two-constraint optimization models using the penalty method into an unconstrained non-smooth optimization one. The non-smoothness nature of the problem has many advantages, but it requires special treatment, which is why we use the primal dual method to solve it, since it is the most appropriate and it is robust in terms of stability, convergence and speed (Lyaqini, Nachaoui and Hadri, 2022). To demonstrate the effectiveness of the proposed approach, several experiments were carried out on numerous UCI benchmarks, medical image and HandPD datasets. These experiments demonstrated the effectiveness and applicability of the proposed approach, with satisfactory results compared to the state of the art. [ABSTRACT FROM AUTHOR]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

Data warehouses exploit On-Line Analytical Processing (OLAP) to make rapid answers for analytical queries. Huge amount of aggregated data within a data warehouse on the one hand, and complex analytical queries raised in a data warehouse on the other hand, increase response time to queries extremely. To solve this problem, a number of views are derived and extracted from original base tables and queries have been answered using them. Since materialization of all possible views is not effective because of limitation of storage and maintenance overhead, selecting an optimal set of views for materialization is crucial to maximize data warehouse performance. In this paper, a game theory based framework for the materialized view selection is proposed. In the proposed framework, query processing and view maintenance costs play a game against each other as two players and continue the game until reach the equilibrium. According to the framework, a new static method, called Game Theory based Materialized View selection (GTMV), has been proposed. Verification of proposed approach has been evaluated using several synthetic and real world datasets. Experimental results show that the GTMV method has better performance comparing previous algorithms and substantially outperform former methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with design of knowledge oriented diagnostic system. Two challenges are addressed. The first one concerns the elicitation of expert practice and the proposition of a methodology for developing four knowledge containers of case based reasoning system. The second one concerns the proposition of a general adaptation phase to reuse case solving diagnostic problems in a different context. In most cases, adaptation methods are application-specific and the challenge in this work is to make a general adaptation method for the field of industrial diagnostics applications. This paper is a contribution to fill this gap in the field of fault diagnostic and repair assistance of equipment. The proposed adaptation algorithm relies on hierarchy descriptors, an implied context model and dependencies between problems and solutions of the source cases. In addition, one can note that the first retrieved case is not necessarily the most adaptable case, and to take into account this report, an adaptation-guided retrieval step based on a similarity measure associated with an adaptation measure is realized on the diagnostic problem. These two measures allow selecting the most adaptable case among the retrieved cases. The two retrieval and adaptation phases are applied on real industrial system called Supervised industrial system of Transfer of pallets (SISTRE). [Copyright &y& Elsevier]

Abstract: This paper addresses constrained and optimal engineering problems solved using an adapted particle swarm optimization (PSO) algorithm. In fact, a specific constraint-handling mechanism is presented. It consists of a closeness evaluation of the solutions to the feasible region. The total constraints violation is introduced as an objective function to minimize. Interval arithmetic is used to normalize the total violations. The resulting objective problem is solved using a simple lexicographic method. The new algorithm is called CVI-PSO for constraint violation with interval arithmetic PSO. The paper provides numerous experimental results based on a well-known benchmark and comparisons with previously reported results. Finally, a case study of the optimal design of an electrical actuator with several model reformulations is detailed. [Copyright &y& Elsevier]

Abstract: The paper deals with a stochastic multi-product sequencing and lot-sizing problem for a line that produces items in lots. Two types of uncertainties are considered: random lead time induced by machine breakdowns and random yield to take into account part rejects. In addition, sequence dependent setup times are also included. This study focuses on maximizing the probability of producing a required quantity of items of each type for a given finite planning horizon. A decomposition approach is used to separate sequencing and lot-sizing algorithms. Previous works have shown that the sequencing sub-problem can be solved efficiently, but the lot-sizing sub-problem remains difficult. In this paper, a memetic algorithm is proposed for this second sub-problem. Computational results show that the algorithms developed can be efficiently used for large scale industrial instances. [Copyright &y& Elsevier]

Particle filter (PF) is introduced to tackle the limitations of the Kalman filter which adopts Gaussian in the state and noise of the system. PFs have the problem of sample impoverishment and one approach to solve this problem is to optimize the proposal distribution shown by particles. This paper introduces a novel evolutionary PF based on Opposition based High Dimensional optimization Algorithm (OHDA) to reposition the particles of PF in high probable regions for estimation. OHDA will preserve the diversity of particles while emphasizing the more informative ones by information sharing and angular movement operators. Opposite particles are introduced in this paper to speed up the convergence of PF. Virtual forward movement by angular movement of OHDA is employed to better guide the search process. The optimized PF can improve the performance of the estimation algorithms in problems such as localization and SLAM. In robot localization problem, particles show the location of the robot in a known environment. For SLAM (Simultaneous Localization And Mapping), particles contain the location of the robot as well as estimated map of the environment. The application of the resulting evolutionary particle filter is tested in both localization and SLAM. Comparing the results of the proposed evolutionary particle filter with other algorithms confirms the efficiency of applying OHDA to PF in terms of improving estimation accuracy in the well-known Victoria park dataset and some other generated test environments. Comparing optimization algorithms on FASTSLAM and UFASTSLAM are PSO, FA, MVO, and MGWO. [ABSTRACT FROM AUTHOR]

This paper presents a control scheme which uses a combination of linear Model Predictive Control (MPC) and a Constraint Satisfaction Problem (CSP) to solve the non-linear operational optimal control of Drinking Water Networks (DWNs). The methodology has been divided into two functional layers: first, a CSP algorithm is used to transfer non-linear DWNs pressure equations into linear constraints on flows and tank volumes, which can enclose the feasible solution set of the hydraulic non-linear problem during the optimization process. Then, a linear MPC with tightened constraints produced in the CSP layer is solved to generate control strategies which optimize the control objectives. The proposed approach is simulated using Epanet to represent the real DWNs. Non-linear MPC is used for validation. To illustrate the performance of the proposed approach, a case study based on the Richmond water network is used and a realistic example, D-Town benchmark network, is added as a supplementary case study. [ABSTRACT FROM AUTHOR]

In this paper, a new sequential learning algorithm is constructed by combining the Online Sequential Extreme Learning Machine (OS-ELM) and Kalman filter regression. The Kalman Online Sequential Extreme Learning Machine (KOSELM) handles the problem of multicollinearity of the OS-ELM, which can generate poor predictions and unstable models. The KOSELM learns the training data one-by-one or chunk-by-chunk by adjusting the variance of the output weights through the Kalman filter. The performance of the proposed algorithm has been validated on benchmark regression datasets, and the results show that KOSELM can achieve a higher learning accuracy than OS-ELM and its related extensions. A statistical validation for the differences of the accuracy for all algorithms is performed, and the results confirm that KOSELM has better stability than ReOS-ELM, TOSELM and LS-IELM. [ABSTRACT FROM AUTHOR]

Software requirements selection is a problem which consists of choosing the set of new requirements which will be included in the next release of a software package. This NP-hard problem is an important issue involving several contradictory objectives which have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to satisfy the highest number of customer requirements. Furthermore, when managing real instances of the problem, the requirements tackled suffer interactions and other restrictions which make the problem even harder. In this paper, a novel multi-objective teaching learning based optimization (TLBO) algorithm has been successfully applied to several instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer׳s satisfaction. In addition, three interaction constraints have been also managed. In this context, the original TLBO algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out in order to prove the effectiveness of the multiobjective proposal. In fact, the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature. [ABSTRACT FROM AUTHOR]

The cloud computing allows to use virtually infinite resources, and seems to be a new promising opportunity to solve scientific computing problems. The MapReduce parallel programming model is a new framework favoring the design of algorithms for cloud computing. Such framework favors processing of problems across huge datasets using a large number of heterogeneous computers over the web. In this paper, we are interested in evaluating how the MapReduce framework can create an innovative way for solving operational research problems. We proposed a MapReduce-based approach for the shortest path problem in large-scale real-road networks. Such a problem is the cornerstone of any real-world routing problem including the dial-a-ride problem (DARP), the pickup and delivery problem (PDP) and its dynamic variants. Most of efficient methods dedicated to these routing problems have to use the shortest path algorithms to construct the distance matrix between each pair of nodes and it could be a time-consuming task on a large-scale network due to its size. We focus on the design of an efficient MapReduce-based approach since a classical shortest path algorithm is not suitable to accomplish efficiently such task. Our objective is not to guarantee the optimality but to provide high quality solutions in acceptable computational time. The proposed approach consists in partitioning the original graph into a set of subgraphs, then solving the shortest path on each subgraph in a parallel way to obtain a solution for the original graph. An iterative improvement procedure is introduced to improve the solution. It is benchmarked on a graph modeling French road networks extracted from OpenStreetMap. The results of the experiment show that such approach achieves significant gain of computational time. [ABSTRACT FROM AUTHOR]

Multi-agent technology is applied in this paper to solve the distribution system service restoration problems. Specifically, switches in the distribution systems are modeled as intelligent agents and organized as local power committees in a distribution system. The advantage of this approach is to utilize the solving ability of a local committee of agents who could coordinate with neighboring committees to achieve an agreement that satisfies the global objective of the power distribution system service restoration problems without relying on a pre-determined centralized optimization algorithm as traditional approaches. The proposed coordination method helps switches in the distributed local power committees to reach a consensus among many proposed service restoration solutions after faults are detected and isolated. We also take the voltage violations into consideration in the search of solutions. Two examples of different power distribution systems are presented to demonstrate how the algorithms of the coordination method can identify the best restoration solution among many proposed local candidate solutions. The simulation results show that the proposed committee-based multi-agent approach yields better performance than the previous methods. [ABSTRACT FROM AUTHOR]

In this paper we develop a greedy randomized adaptive search procedure (GRASP) for solving a transportation problem arising in disaster relief situations. The problem is to satisfy the demand of a given set of customers through creating some open routes by using a limited number of vehicles located at the central depot. We assume that the demand of each customer to be directly satisfied by being visited on a route or just being close to a visited customer. Two mathematical models are proposed for the mentioned problem and enriched by introducing some valid inequalities. The performance of the developed algorithm is tested on different sets of instances. The computational results indicate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

The Fuzzy C-means (FCM) clustering algorithm is an effective method for image segmentation. Non-local spatial information considers more redundant information of the image thus is more robust to noise. However, under-segmentation of non-local spatial information may exist with higher noise density. The number of iteration steps is also significant in FCM, and employing membership linking can effectively reduce the number of iteration steps. Nonetheless, when there are outliers in the membership degree, the membership linking can make the algorithm converge prematurely before reaching the optimum, affecting segmentation performance. This paper presents a fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking (FSC_LNML). Firstly, local variance templates are utilized to eliminate the under-segmentation of non-local information, and local variance & non-local information are integrated into the FCM objective function to improve robustness. Secondly, the mean membership linking is employed as the denominator of the objective function to reduce the number of iterations and solve the problem that the algorithm converges early before reaching the optimum when the membership has an outlier. Thirdly, the absolute intensity difference between the original image and the local variance & non-local information and its inverse are used to adaptively constrain the original image and the local variance & non-local information. Finally, the concept of the subspace is introduced to adaptively assign appropriate weights to each dimension of the image to improve the segmentation performance of color images. The simulation results on noisy grayscale images and noisy color images show that the efficiency of the proposed method FSC_LNML is better than other fuzzy-based clustering algorithms. The convergence proof of the algorithm is also presented. • Introducing a local variance template in the non-local spatial information to eliminate the under-segmentation of the non-local spatial information. • Using the mean membership linking as the denominator of the objective function to reduce the iteration steps and solve the convergence problem of the objective function before the algorithm reaches the optimal solution. • Assigning appropriate weights to each image dimension to improve the segmentation performance of color images. [ABSTRACT FROM AUTHOR]

Abstract: Segmenting Foreground objects from a video sequence is a key processing and critical step in video analysis such as object detection and tracking. Several Foreground detection techniques and edge detectors have been developed till now but the problem is that it is very difficult to obtain an optimal foreground due to the interference from the factors like weather, light, shadow and clutter. Background subtraction is used in many of the applications, where the background noise appears at fixed places and also, when it is used for long image sequence, there may be more accumulated error in the foreground. Optical flow is the velocity field which warps one image into another (usually very similar) image where the background noise appears randomly. It covers long distance and the background noise due to brightness change is less which results in less accumulate error percentage. However, it cannot get rid of light influences which result in background noises. This paper proposes a new foreground detection approach to overcome these issues by combining the background subtraction algorithm and optical flow along with SMED (Separable Morphological Edge Detector) to reduce the background noises. SMED has robustness to light changing and capable of detecting even slight movement in the video sequence. The proposed work is highly accurate in detecting the moving objects in various scenarios such as fast moving objects, slow moving objects and even moving objects in dynamic scenes, where both the foreground and background changes. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models is addressed. This problem involves both the estimation of the parameters of the affine sub-models and the hyperplanes defining the partitions of the state-input regression. In fact, we propose the use of the Chiu's clustering algorithm in order to overcome the main drawbacks of the existing methods which are the poor initialization and the presence of outliers. In addition, our approach is able to generate automatically the number of sub-models. Simulation results are presented to illustrate the performance of the proposed method. An application of the developed approach to an olive oil esterification reactor is also suggested in order to validate simulation results. [Copyright &y& Elsevier]

Abstract: Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm — multiple-domain NMF (MDNMF) — was evaluated on two challenging multiple-domain learning problems — multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. [Copyright &y& Elsevier]

The growing energy demand and environmental consciousness provoke the conventional single-objective optimization framework no longer satisfies new power system planning and control requirements. The number of optimization objectives being considered in power system optimization is increasing, necessitating the development of many-objective Optimal Power Flow (OPF) problems and the development of solution methods. In this paper, the fitness functions for the Many-Objective OPF (MaO-OPF) problem have been formulated, and a new Many-Objective Gradient-Based Optimizer (MaOGBO) based on reference point strategy is proposed to solve the MaO-OPF problem. The objectives of the MaO-OPF problem is to minimize the Reactive Power Loss (RPL), Active Power Loss (APL), Voltage Magnitude Deviation (VMD), Voltage Stability Indicator (VSI), Total Emission (TE), and the Total Fuel Cost (TFC) by satisfying different complex equality and inequality constraints. In the proposed MaOGBO, a reference point strategy is employed to acquire evenly spread Pareto -optimal solutions in each objective space. In order to improve the effectiveness of Pareto solutions, a mixed-multi-constraint handling approach is also implemented. In order to deal with the complex non-linear constraints, the process utilizes both the repair technique and penalty function. Besides, a fuzzy-based membership function strategy is also applied to locate the Best Compromise Solution (BCS) from the Pareto -optimal analytical solution. In order to validate the effectiveness of the proposed MaOGBO, DTLZ and MaF benchmark test suites are considered. In addition, a standard Institute of Electrical and Electronics Engineers (IEEE) bus test systems, such as IEEE-30/IEEE-57/IEEE-118 with different case studies, are also considered to assess the performance of the proposed algorithm. The obtained results are compared with other state-of-the-art algorithms, and the proposed MaOGBO proves the superiority over other competitors for most of the selected problems, including MaO-OPF. Finally, the proposed MaOGBO is also validated on large-scale Algerian 59-bus power systems and proved its superiority in handling realistic systems. This research is further supported up with extra online service and guidance at https://premkumarmanoharan.wixsite.com/mysite. • MaOGBO is developed using a reference-point mechanism to obtain the solution diversity and faster convergence. • MaOGBO is combined with a multi-constraint handling mechanism and fuzzy membership-based best compromise solution strategy. • The performance of the MaOGBO is validated on 22 benchmark test suites and three test frameworks of MaO-OPF problems, and also validated on large scale Algerian 59-bus real-time system. • Performance metrics, such as GD, IGD, SP, HV, and RT are calculated for all selected problems. • Three different statistical significance tests are carried out to assess the performance of MaOGBO. [ABSTRACT FROM AUTHOR]

Abstract: Two major goals in multi-objective optimization are to obtain a set of nondominated solutions as closely as possible to the true Pareto front (PF) and maintain a well-distributed solution set along the Pareto front. In this paper, we propose a teaching-learning-based optimization (TLBO) algorithm for multi-objective optimization problems (MOPs). In our algorithm, we adopt the nondominated sorting concept and the mechanism of crowding distance computation. The teacher of the learners is selected from among current nondominated solutions with the highest crowding distance values and the centroid of the nondominated solutions from current archive is selected as the Mean of the learners. The performance of proposed algorithm is investigated on a set of some benchmark problems and real life application problems and the results show that the proposed algorithm is a challenging method for multi-objective algorithms. [Copyright &y& Elsevier]

Abstract: This article is betrothed to serve as a continuation of the emerging swarm techniques to solve supply chain problems. Our aim is to map some of the pressing research challenges contributed by the artificial intelligence community and to develop an improved algorithm: Co-evolutionary immuno-particle swarm optimisation with penetrated hyper-mutation (COIPSO-PHM). In this paper, we proposed a new algorithm which uses clonal selection approach in particle swarm optimisation by embedding co-evolutionary theory to solve the problem of inventory replenishment in distributed plant–warehouse–retailer system. Constraint handling is explicitly taken care by implanting augmented lagrangian concept. To demonstrate the efficiency of the algorithm, its performance are evaluated and compared on 10 benchmarked problems (made constrained problem via random initialisation in the infeasible zone) including functions with uni-modalities as well as multi-modalities. The result follows shows superior performance of the algorithm in every respect. [Copyright &y& Elsevier]

Abstract: This paper deals with the preliminary design problem when the product is modeled as an analytic model. The analytic models based method aims to use mathematical equations to address both multi-physic and economic characteristics of a product. The proposed approach is to convert the preliminary design problem into a global constrained optimization problem. The objective is to develop powerful optimization methods enough to handle complex analytical models. We propose to adapt an approach to solve this problem based on interval analysis, constraint propagation and model reformulation. In order to understand the optimization algorithm used for engineering design problems, some basic definitions and properties of interval analysis are introduced. Then, the basic optimization algorithms for both unconstrained and constrained problems are introduced and illustrated. The next section introduces the reformulation technique as main accelerating device. An application of the reformulation device and its global optimization algorithm on the optimal design of electrical actuators is presented. [Copyright &y& Elsevier]

Abstract: This paper presents three proposals of multiobjective memetic algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. These three proposals are, respectively, based on evolutionary computation, ant colony optimisation, and greedy randomised search procedure. Different variants of these memetic algorithms have been developed and compared in order to determine the most suitable intensification–diversification trade-off for the memetic search process. Once a preliminary study on nine well-known problem instances is accomplished with a very good performance, the proposed memetic algorithms are applied considering real-world data from a Nissan plant in Barcelona (Spain). Outstanding approximations to the pseudo-optimal non-dominated solution set were achieved for this industrial case study. [Copyright &y& Elsevier]

Abstract: Selection of the optimal values of different operating parameters is of utmost importance for enhancing the performance of various non-traditional machining (NTM) processes. The performance measures (responses) of different NTM processes usually include metal removal rate, surface roughness, radial overcut, tool wear rate, heat affected zone, etc. In this paper, artificial bee colony (ABC) algorithm is employed to search out the optimal combinations of different operating parameters for three widely used NTM processes, i.e. electrochemical machining, electrochemical discharge machining and electrochemical micromachining processes. Both the single and multi-objective optimization problems for the considered NTM processes are solved using this algorithm. The results obtained while applying the ABC algorithm for parametric optimization of these three NTM processes are compared with those derived by the past researchers, which prove the applicability and suitability of the ABC algorithm in enhancing the performance measures of the considered NTM processes. [Copyright &y& Elsevier]

Abstract: In this paper, a Multi-Swarm Self-Adaptive and Cooperative Particle Swarm Optimization (MSCPSO) based on four sub-swarms is presented. In the proposed algorithm, several strategies are employed to avoid falling into local optimum, improve the diversity and achieve better solution. Particles in each sub-swarms share the only global historical best optimum to enhance the cooperative capability. Besides, the inertia weight of a particle in each sub-swarms is modified, which is subject to the fitness information of all particles, and the adaptive strategy is employed to control the influence of the historical information to create a more potential search ability. To effectively keep the balance between the global exploration and the local exploitation, the particle in each takes advantage of the shared information to maintain cooperation with each other and guides its own evaluation. On the other hand, in order to increase the diversity of the particles and avoid falling into a local optimum, a various diversity operation is adopted to guide the particles to jump out of the local optimum and achieve the global best position smoothly. The proposed method was applied to some well-known benchmarks; the results demonstrated good performances of MSCPSO in solving the complex multimodal functions. [Copyright &y& Elsevier]

Abstract: This paper presents a constrained Self-adaptive Differential Evolution (SaDE) algorithm for the design of robust optimal fixed structure controllers with uncertainties and disturbance. Almost all real world optimization problems have constraints which should be satisfied along with the best optimal solution for the problem. In evolutionary algorithms (EAs) the presence of constraints reduces the feasible region and complicates the search process. Therefore, a suitable method to handle the constraints must also be executed. In the SaDE algorithm, four mutation strategies and the control parameter CR are self-adapted. Self-adaptive Penalty (SP) method is introduced into the SaDE algorithm for constraint handling. The performance of SaDE algorithm is demonstrated on the design of robust optimal fixed structure controller of three systems, namely the linearized magnetic levitation system, F-8 aircraft linearized model and a SISO plant. For the comparison purpose, reported results of constrained PSO algorithm and five DE algorithms with different strategies and parameter values are taken into account. Statistical performance in 20 independent runs is considered to compare the performance of algorithms. From the obtained results, it is observed that SaDE algorithm is able to self-adapt the mutation strategy and the crossover rate and hence performs better than the other variants of DE and the constrained PSO algorithm. Better performance of SaDE is achieved by sustained maintenance of diversity throughout the evolutionary process thus producing better individuals consistently. This also aids the algorithm to escape from local optima thereby avoiding premature convergence. [Copyright &y& Elsevier]

Tree-Seed Algorithm (TSA) is a meta-heuristic optimization algorithm with good performance in solving continuous optimization problems. However, there is an imbalance between its exploration and exploitation when solving complex problems, mainly lack exploration. To overcome this problem, this paper proposes two tree migration mechanisms: triple-learning-based migration mechanism and sine-random-distribution migration mechanism. The target tree position is migrated by learning from the first three trees in the current iteration. The sine function is added to the tree migration formula to enhance the randomness of tree distribution. In order to verify these migration mechanisms, Triple Tree-Seed Algorithm (TriTSA) has been proposed and compared with TSA on 30 well-known test functions from IEEE CEC 2014. In addition, STSA, SCA, PSO, ABC, Jaya, and TLBO are adopted in some comparative experiments on different dimensions. The experiments show that the tree migration mechanism can improve the optimization capability of the original algorithm effectively. On all 30 benchmark test functions, TriTSA outperforms TSA on 10, 30, 50, and 100 dimensions by 70%, 90%, 90%, and 97% respectively. Finally, the proposed TriTSA is compared with TSA, ABC, PSO, and SCA on solving two classical engineering design problems. It is proved that the proposed algorithm is more applicable in solving practical problems. [ABSTRACT FROM AUTHOR]

Differential evolution is an effective and practical approach that is widely applied for solving global optimization problems. Nevertheless, its effectiveness and scalability are decreased when the problems' dimension is increased. Hence, this paper is devoted to proposing a novel DE algorithm named quantum-based avian navigation optimizer algorithm (QANA) inspired by the extraordinary precision navigation of migratory birds during long-distance aerial paths. In the QANA, the population is distributed by partitioning into multi flocks to explore the search space effectively using proposed self-adaptive quantum orientation and quantum-based navigation consisted of two mutation strategies, DE/quantum/I and DE/quantum/II. Except for the first iteration, each flock is assigned using an introduced success-based population distribution (SPD) policy to one of the quantum mutation strategies. Meanwhile, the information flow is shared through the population using a new communication topology named V-echelon. Furthermore, we introduce two long-term and short-term memories to provide meaningful knowledge for partial landscape analysis and a qubit-crossover operator to generate the next search agents. The effectiveness and scalability of the proposed QANA were extensively evaluated using benchmark functions CEC 2018 and CEC 2013 as LSGO problems. The results were statistically analyzed by the Wilcoxon signed-rank sum test, ANOVA, and mean absolute error tests. Finally, the applicability of the QANA to solve real-world problems was evaluated by four engineering problems. The experimental results and statistical analysis prove that the QANA is superior to the competitor DE and swarm intelligence algorithms in test functions CEC 2018 and CEC 2013, with overall effectiveness of 80.46% and 73.33%, respectively. • Proposing an optimizer algorithm (QANA) inspired by migratory birds' navigation behaviors. • Introducing LTM and STM memories to share information and partial landscape analysis. • Introducing a V-echelon topology to spread information flow throughout search agents. • Introducing a quantum mutation strategy equipped by two meaningful search strategies. • The proposed QANA optimizer is very competitive and superior to compared algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Genetic algorithm (GA) has been used as a conventional method for classifiers to evolve solutions adaptively for classification problems. In this paper, a new approach using class decomposition is proposed to improve the performance of GA-based classifiers. A classification problem is fully partitioned into several class modules in the output domain and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently and the results obtained are integrated and evolved further for a final solution. A scheme based on Fisher''s linear discriminant (FLD) computation is used to estimate the difficulty of separating two classes. Based on the FLD information derived, different integration approaches are proposed and their performance is compared. The experiment results on a benchmark data set show that class decomposition can achieve higher classification rate than the normal GA and FLD-based integration improves the classification accuracy further. [Copyright &y& Elsevier]

In practical applications, solving multimodal multiobjective optimization problems (MMOPs), which have multiple Pareto optimal sets (PSs) in the decision space mapping to the same Pareto front (PF) in the objective space, has great significance for decision makers. The issue of how to maintain diversity in both decision space and objective space remains a key problem for existing multimodal multiobjective evolutionary algorithms. To address this issue, a novel grey prediction evolution algorithm for multimodal multiobjective optimization, termed MMGPE, is proposed in this paper. This is the first time that the grey prediction evolution algorithm (GPE), which is a recently proposed competitive optimization algorithm with strong exploration capability, is improved for MMOPs. These improvements are conducted in the following four aspects: (1) an initialization operator based on particle swarm optimization, (2) an adaptive parameter setting strategy depending on the domain of decision variables of MMOPs, (3) an accelerating convergence mechanism inspired by the niche principle, and (4) an environmental selection operator based on a nondominated sorting mechanism and a special crowding distance approach. The performance of MMGPE is compared with two state-of-the-art multiobjective evolutionary algorithms and four multimodal multiobjective evolutionary algorithms on 11 multimodal multiobjective test functions. MMGPE is also applied to solve a practical problem. The results show the MMGPE's effectiveness and superiority in achieving the goal of finding multiple PSs while obtaining a well-distributed PF. [ABSTRACT FROM AUTHOR]