A Multi-Strategy Adaptive Coati Optimization Algorithm for Constrained Optimization Engineering Design Problems

Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. Howeve...

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Vydané v:	Biomimetics (Basel, Switzerland) Ročník 10; číslo 5; s. 323
Hlavní autori:	Wu, Xingtao, Ding, Yunfei, Wang, Lin, Zhang, Hongwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 16.05.2025 MDPI
Predmet:	Algorithms Behavior CEC2017 coati vigilante mechanism Convergence Design engineering engineering design optimization problems Exploitation Heuristic Hunting Lévy flight Mathematical optimization multi-strategy adaptive coati optimization algorithm nonlinear step size inertia factor Optimization algorithms Optimization techniques CEC2017 multi-strategy adaptive coati optimization algorithm coati vigilante mechanism engineering design optimization problems nonlinear step size inertia factor Lévy flight
ISSN:	2313-7673, 2313-7673
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Shrnutí:	Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. However, the effectiveness of the COA is compromised by the homogeneity of its initial population and its reliance on random strategies for prey hunting. To address these issues, a multi-strategy adaptive coati optimization algorithm (MACOA) is presented in this paper. Firstly, Lévy flights are incorporated into the initialization phase to produce high-quality initial solutions. Subsequently, a nonlinear inertia weight factor is integrated into the exploration phase to bolster the algorithm’s global search capabilities and accelerate convergence. Finally, the coati vigilante mechanism is introduced in the exploitation phase to improve the algorithm’s capacity to escape local optima. Comparative experiments with many existing algorithms are conducted using the CEC2017 test functions, and the proposed algorithm is applied to seven representative engineering design problems. MACOA’s average rankings in the three dimensions (30, 50, and 100) were 2.172, 1.897, and 1.759, respectively. The results show improved optimization speed and better performance.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2313-7673 2313-7673
DOI:	10.3390/biomimetics10050323