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Multi-objective boxing match algorithm for multi-objective optimization problems
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AI
DOI：10.1016/j.eswa.2023.122394.png)
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Abstract
En 中文
In the last two decades, due to having fast computation after inventing computers and also considering realworld optimization problems, research on developing new algorithms for problem having more than one objective have been one of the appealing and attractive topics both for academia and industrial practitioners. By this motivation, we introduce a Multi-Objective Boxing Match Algorithm (MOBMA) in this paper. The proposed algorithm studies the multi-objective version of the Boxing Match Algorithm (BMA) by incorporating a unique search strategy and new solutions-producing mechanism, enhancing the algorithm's capability for exploration and exploitation phases. Besides, its performance is analyzed with famous and capable multi-objective metaheuristics. We consider ten multi-objective benchmarks and three classical engineering problems. Statistical analyses are also conducted on the benchmark test functions from three engineering design problems. This study shows the superior performance of the proposed algorithm, considering both quantitative and qualitative analyses.
Keywords:
Meta-heuristics
Multi-objective optimization
Boxing match algorithm
Multi-objective evolutionary algorithm
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
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