TY - JOUR
T1 - Multi-objective boxing match algorithm for multi-objective optimization problems
AU - Tavakkoli-Moghaddam, Reza
AU - Hosein Akbari, Amir
AU - Tanhaeean, Mehrab
AU - Moghdani, Reza
AU - Gholian-Jouybari, Fatemeh
AU - Hajiaghaei-Keshteli, Mostafa
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/4/1
Y1 - 2024/4/1
N2 - In the last two decades, due to having fast computation after inventing computers and also considering real-world 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.
AB - In the last two decades, due to having fast computation after inventing computers and also considering real-world 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.
KW - Boxing match algorithm
KW - Meta-heuristics
KW - Multi-objective evolutionary algorithm
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85175853529&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2023.122394
DO - 10.1016/j.eswa.2023.122394
M3 - Article
AN - SCOPUS:85175853529
VL - 239
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
M1 - 122394
ER -