A new multi-objective optimization algorithm combined with opposition-based learning

•A new multi-objective optimization method used OBL strategy, WOA and DE algorithms•It combines DE and the OBL to improve the performance of the WOA•The MWDEO results outperformed all other algorithms in most of the test problems•32 multi-objective test problems are used in the experiments and CEC20...

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Veröffentlicht in:Expert systems with applications Jg. 165; S. 113844
Hauptverfasser: Ewees, Ahmed A., Abd Elaziz, Mohamed, Oliva, Diego
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.03.2021
Elsevier BV
Schlagworte:
Differential evolution
Evolutionary algorithms
Evolutionary computation
Machine learning
Multi-objective optimization
Multiple objective analysis
Opposition-based learning
Optimization algorithms
Pareto optimization
Whale optimization algorithm
Differential evolution
Multi-objective optimization
Whale optimization algorithm
Opposition-based learning
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•A new multi-objective optimization method used OBL strategy, WOA and DE algorithms•It combines DE and the OBL to improve the performance of the WOA•The MWDEO results outperformed all other algorithms in most of the test problems•32 multi-objective test problems are used in the experiments and CEC2017 problems The optimization problems are divided into a single objective and multi-objective. Single objective optimization has only one objective function; whereas, multi-objective optimization has multiple objective functions that generate the Pareto set; therefore, solving a multi-objective problem is a challenging problem. This paper presents a new multi-objective optimization method (called MWDEO) based on improved whale optimization algorithm (WOA) by combining the differential evolution (DE) algorithm and the opposition-based learning (OBL). The MWDEO uses the WOA to perform a global exploration, whereas DE is used to exploit the search space; while the OBL is applied to improve the exploration and exploitation by generating the opposite values. The proposed algorithm is evaluated using 32 multi-objective test problems besides a set of benchmark problems of CEC’2017. The experimental results are compared with nine state-of-the-art multi-objective methods. The analysis of the results showed that the proposed MWDEO outperformed all other algorithms in most of the test problems which indicates that the proposed MWDEO is competitive and effective in solving different types of multi-objective problems.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113844