A Self-Organizing Multimodal Multi-Objective Coati Optimization Algorithm
نویسندگان
چکیده
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 testament to exceptional performance and versatility. Compared other algorithms, COA been proven possess excellent global local search capabilities. This paper introduces novel self-organizing multimodal multi-objective (MMOCOA) designed specifically tackle proposed aims effectively handle the complexities associated with such problems by incorporating mechanisms into optimization framework. Primarily, MMOCOA utilizes speciation method primary approach identify Pareto optimal solutions. tactic can establish stable niches continually updates them actively preserve Furthermore, an improved self-organization mechanism enhance generation speed of niches. Additionally, incorporates non-dominated sorting specialized crowding distance technique diversity both decision objective space. To assess effectiveness MMOCOA, this study presents comprehensive evaluation using eleven test functions. benchmarked against five state-of-the-art algorithms. experimental results highlight superior it demonstrates capability discover larger number solutions compared algorithms under consideration.
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ژورنال
عنوان ژورنال: Advances in computer, signals and systems
سال: 2023
ISSN: ['2371-882X', '2371-8838']
DOI: https://doi.org/10.23977/acss.2023.070703