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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Veröffentlicht in:Biomimetics (Basel, Switzerland) Jg. 10; H. 5; S. 323
Hauptverfasser: Wu, Xingtao, Ding, Yunfei, Wang, Lin, Zhang, Hongwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 16.05.2025
MDPI
Schlagworte:
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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Zusammenfassung: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.
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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics10050323