Journal
APPLIED SOFT COMPUTING
Volume 145, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2023.110585
Keywords
Multi-objective bald eagle search algorithm; Pareto optimal solutions; Benchmark function; Engineering design problems; Metaheuristic
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In this paper, a multi-objective bald eagle search algorithm (MOBES) is proposed, which introduces an archive mechanism and elite selection strategy to enhance efficiency. The MOBES outperforms its competitors in terms of convergence, diversity, and distribution of solutions on CEC 2020 benchmark functions. It is also proven to be more competitive in handling challenging multi-objective optimization problems in real-world engineering design.
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.& COPY; 2023 Elsevier B.V. All rights reserved.
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