In many-objective optimization problems (MaOPs), forming sound tradeoffs between convergence and diversity for the environmental selection of evolutionary algorithms is a laborious task. In ...particular, strengthening the selection pressure of population toward the Pareto-optimal front becomes more challenging, since the proportion of nondominated solutions in the population scales up sharply with the increase of the number of objectives. To address these issues, this paper first defines the nondominated solutions exhibiting evident tendencies toward the Pareto-optimal front as prominent solutions, using the hyperplane formed by their neighboring solutions, to further distinguish among nondominated solutions. Then, a novel environmental selection strategy is proposed with two criteria in mind: 1) if the number of nondominated solutions is larger than the population size, all the prominent solutions are first identified to strengthen the selection pressure. Subsequently, a part of the other nondominated solutions are selected to balance convergence and diversity and 2) otherwise, all the nondominated solutions are selected; then a part of the dominated solutions are selected according to the predefined reference vectors. Moreover, based on the definition of prominent solutions and the new selection strategy, we propose a hyperplane assisted evolutionary algorithm, referred here as hpaEA , for solving MaOPs. To demonstrate the performance of hpaEA , extensive experiments are conducted to compare it with five state-of-the-art many-objective evolutionary algorithms on 36 many-objective benchmark instances. The experimental results show the superiority of hpaEA which significantly outperforms the compared algorithms on 20 out of 36 benchmark instances.
In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving ...complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
•Atomic Orbital Search is proposed as a novel metaheuristic algorithm.•Benchmark functions are tested and engineering design problems are solved.•Comparative analyses is conducted for performance ...evaluation of the new algorithm.•Statistical analysis is conducted for competitive investigation of the new algorithm.
In this paper, the Atomic Orbital Search (AOS) is proposed as a novel metaheuristic algorithm for optimization purposes. The main concept of this algorithm is based on some principles of quantum mechanics and the quantum-based atomic model in which the general configuration of electrons around nucleus is in perspective. In order to evaluate the performance of this algorithm, a total number of 20 unconstrained mathematical test functions are utilized with different dimensions of 2–100 while a maximum number of 150,000 function evaluations is considered with 100 independent optimization runs for statistical purposes. A complete statistical analysis is also conducted by utilization of the Kolmogorov Smirnov, Wilcoxon and the Kruskal Wallis tests while 8 metaheuristics are also utilized as alternatives for comparative purposes. The latest Competitions on Evolutionary Computation (CEC) regarding the single objective real-parameter numerical optimization (CEC 2017) including 30 benchmark test functions is also considered in which the capability of the proposed algorithm is compared to the most state−of-the−art algorithms in the optimization field. In addition, a total number of 5 constrained engineering design problems are utilized as design examples including some of the constrained optimization problems of the recent Competitions on Evolutionary Computation (CEC 2020). The obtained results of the AOS algorithm in dealing with the constraint problems are compared to the results of different standard, improved and hybrid metaheuristic algorithms from the literature. The obtained results demonstrate that the proposed AOS algorithm provides very outstanding results in dealing with the mathematical and engineering design problems.
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•A review of state-of-the-art feature extraction methods from electroencephalogram signals.•A new framework using evolutionary algorithms to find the most optimal features set and ...channels.•Comprehensive experimental results based on two public datasets and one newly collected dataset.
There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications.
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