Multi-objective Butterfly Optimization Algorithm for Solving Constrained Optimization Problems

Mohammed M. Ahmed (), Aboul Ella Hassanien () and Mincong Tang ()
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Mohammed M. Ahmed: University of Sadat City
Aboul Ella Hassanien: Scientific Research Group in Egypt (SRGE)
Mincong Tang: Beijing Jiaotong University

A chapter in LISS 2021, 2022, pp 389-400 from Springer

Abstract: Abstract Constraint functions are considered one of the main challenges that face Multi-objective optimization algorithm so in this paper proposes new method from multi-objective optimization algorithm that called multi-objective butterfly optimization algorithm (MOBOA) which is based on Butterfly Optimization Algorithm (BOA). The proposed algorithm MOBOA extract non-dominated solutions which store in external archive in order to maintain the diversity between the optimal set of solutions. To assess and validate the MOBOA’s effectiveness using a set of standard constrained metrics multi-objective benchmark problems that have various characteristics of Pareto front (PF). The results demonstrate that MOBOA is able to find both of better spread of solutions and convergence near the true PF. Furthermore, the quantitative and qualitative results prove that MOBOA provides high convergence and providing very competitive results in solving challenging real-world problems efficiency compared to other algorithms.

Keywords: Butterfly optimization algorithm; Multi-objective optimization; Pareto optimal solutions; Evolutionary algorithms; Constrained optimization problems (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-16-8656-6_36

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DOI: 10.1007/978-981-16-8656-6_36

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