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Modified Butterfly Optimization Algorithm based on Convergence Factor and Disturbance Strategy

Published: 04 April 2023 Publication History

Abstract

The butterfly optimization algorithm (BOA) is a relatively new optimization technology with strong competitiveness compared with other meta-heuristic algorithms. However, BOA has shortcomings in convergence accuracy, convergence speed, and jumping out of local optimum. This paper proposes an improved Butterfly Optimization Algorithm based on convergence factor and disturbance strategy (LCD-BOA) to solve these shortcomings. Based on the BOA algorithm, the Levy flight strategy and disturbance factor strategy are added to improve the exploration ability of the algorithm. In addition, the convergence factor strategy is added to improve the exploitation ability of the algorithm further so that the exploration and exploitation of the algorithm are balanced as far as possible. Finally, the experimental results on 11 benchmark functions prove the effectiveness of the improved algorithm. The experimental results show that the improved algorithm significantly improves the performance of BOA compared with other meta-heuristic algorithms.

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        ICNCC '22: Proceedings of the 2022 11th International Conference on Networks, Communication and Computing
        December 2022
        365 pages
        ISBN:9781450398039
        DOI:10.1145/3579895
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Published: 04 April 2023

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