An effective multi-objective bald eagle search algorithm for solving engineering design problems

In this paper, a multi-objective bald eagle search algorithm (MOBES) is proposed. The MOBES introduces an archive mechanism to store the non-dominated solutions obtained by the algorithm. When the archive overflows, remove the most crowded solutions by using the roulette method. The MOBES also adds...

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Veröffentlicht in:Applied soft computing Jg. 145; S. 110585
Hauptverfasser: Zhang, Yunhui, Zhou, Yongquan, Zhou, Guo, Luo, Qifang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2023
Schlagworte:
Benchmark function
Engineering design problems
Metaheuristic
Multi-objective bald eagle search algorithm
Pareto optimal solutions
Pareto optimal solutions
Benchmark function
Metaheuristic
Engineering design problems
Multi-objective bald eagle search algorithm
ISSN:1568-4946
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Zusammenfassung:In this paper, a multi-objective bald eagle search algorithm (MOBES) is proposed. The MOBES introduces an archive mechanism to store the non-dominated solutions obtained by the algorithm. When the archive overflows, remove the most crowded solutions by using the roulette method. The MOBES also adds elite selection strategy to guide other individuals to optimize by selecting elite individuals in the population. The efficiency of MOBES is validated on CEC 2020 benchmark functions, and the results demonstrate that the proposed algorithm is more efficient than its competitors in terms of convergence, diversity and distribution of solutions. The MOBES is also applied to two-objective, tri-objective and four-objective engineering design problems in real world. The results show its superiority in handling challenging multi-objective optimization problems with unknown true Pareto optimal solutions and fronts, and it is more competitive than other algorithms. •An efficient multi-objective bald eagle search (MOBES) algorithm is proposed.•The MOBES introduces an archive mechanism to store the non-dominated solutions.•The MOBES adds elite selection strategy to guide other individuals to optimize by selecting elite individuals in the population.•The CEC2020 functions, two-objective, tri-objective and four-objective engineering design problems are utilized for verification.•The experimental results show that the MOBES is more competitive than other algorithms.
ISSN:1568-4946
DOI:10.1016/j.asoc.2023.110585