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  4. Vol 7, Issue 7, 2023

A Self-Organizing Multimodal Multi-Objective Coati Optimization Algorithm

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DOI: 10.23977/acss.2023.070703 | Downloads: 54 | Views: 560

Author(s)

Waixing Deng 1, Yuanbin Mo 2, Liang Deng 3

Affiliation(s)

1 School of Artificial Intelligence, Guangxi Mingzu University, Nanning, 530006, China
2 Guangxi Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning, 530006, China
3 School of Information Science, Guangdong University of Finance and Economics, Guangzhou, 510000, China

Corresponding Author

Yuanbin Mo

ABSTRACT

The Coati Optimization Algorithm (COA) has emerged as a prominent evolutionary algorithm renowned for its efficacy in addressing real-world problems. Its wide-ranging applicability across diverse domains is a testament to its exceptional performance and versatility. Compared to other evolutionary algorithms, COA has been proven to possess excellent global and local search capabilities.  This paper introduces a novel self-organizing multimodal multi-objective Coati Optimization Algorithm (MMOCOA) designed specifically to tackle multimodal multi-objective problems. The proposed algorithm aims to effectively handle the complexities associated with such problems by incorporating self-organizing mechanisms into the Coati optimization framework. Primarily, MMOCOA utilizes a self-organizing speciation method as its primary approach to identify the Pareto optimal solutions. This speciation tactic can establish stable niches and continually updates them to actively search for and preserve the optimal Pareto solutions. Furthermore, an improved self-organization mechanism is proposed to enhance the generation speed of the niches.  Additionally, MMOCOA incorporates a non-dominated sorting method and a specialized crowding distance technique to effectively preserve the diversity of both the decision and objective space. To assess the effectiveness of MMOCOA, this study presents a comprehensive evaluation using eleven multimodal multi-objective test functions. Additionally, MMOCOA is benchmarked against five state-of-the-art multimodal multi-objective optimization algorithms. The experimental results highlight the superior performance of MMOCOA, as it demonstrates the capability to discover a larger number of Pareto solutions compared to the other algorithms under consideration.

KEYWORDS

Coati Optimization Algorithm, Multimodal multi-objective, Self-organizing speciation, Niching technique

CITE THIS PAPER

Waixing Deng, Yuanbin Mo, Liang Deng, A Self-Organizing Multimodal Multi-Objective Coati Optimization Algorithm. Advances in Computer, Signals and Systems (2023) Vol. 7: 17-29. DOI: http://dx.doi.org/10.23977/acss.2023.070703.

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