Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms

Chengcheng Chen, Xianchang Wang, Helong Yu, Mingjing Wang and Huiling Chen

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 188, issue C, 291-318

Abstract: Evolutionary population-based methods have found their applications in dealing with many real-world simulation experiments and mathematical modelling problems. The Moth-flame optimization (MFO) algorithm is one of the swarm intelligence algorithms and it can be used with constrained and unknown search spaces. However, there are still some defects in its performance, such as low solution accuracy, slow convergence, and insufficient exploration capability. This study improves the basic MFO algorithm from the perspective of improving exploration capability and proposes a hybrid swarm-based algorithm called SMFO. The essential notion is to further explore and scan the feature space with taking advantages of the sine cosine strategy. We methodically investigated the efficacy, solutions, and optimization compensations of the developed SMFO using more than a few demonstrative benchmark tests, together with unimodal, multimodal, hybrid and composition tasks, and two widely applied engineering test problems. The simulations point towards this fact that the diversification and intensification inclinations of the original MFO and its convergence traits are fortunately upgraded. The findings and remarks show that the suggested SMFO is a favourable algorithm and it can show superior efficacy compared to other techniques.

Keywords: Moth-flame optimization algorithm; Global optimization; Swarm intelligence; Sine cosine algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:188:y:2021:i:c:p:291-318

DOI: 10.1016/j.matcom.2021.04.006

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