Improve Coati Optimization Algorithm for Solving Constrained Engineering Optimization Problems

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Heming Jia, Shengzhao Shi, Di Wu, Honghua Rao, Jinrui Zhang, Laith Abualigah
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Abstract

Abstract The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (1) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half waits beneath to catch it; (2) Coatis avoidance predators behavior. Which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm's performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coatis optimization algorithm (ICOA) to enhance the algorithm's efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm's exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm's global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems.
求解约束工程优化问题的改进Coati算法
coati优化算法(COA)是2022年提出的一种元启发式优化算法。它根据浣熊的习性和社会行为建立数学模型:(1)在浣熊的群体组织中,一半的浣熊爬上树去追赶猎物,另一半在树下等待猎物;(2)浣熊躲避捕食者行为。这使得算法具有较强的全局搜索能力。然而,在我们的实验过程中,我们发现了增强算法性能的机会。当面对复杂的优化问题时,一定的局限性浮出水面。就像长鼻浣熊在接近最优解时逐渐缩小搜索范围一样,COA算法可能会导致收敛速度降低,并有陷入局部最优的风险。为了提高算法的效率,本文提出了一种改进的coatis优化算法(ICOA)。通过基于声音的搜索包络策略,浣熊可以更快更准确地捕获猎物,从而使算法更快地收敛。通过使用体力消耗策略,浣熊在被追逐时可以有更多的逃脱选择,从而增强算法的探索能力和逃离局部最优的能力。最后,加入基于镜头对立的学习策略,提高算法的全局性能。为了验证ICOA的性能,我们使用IEEE CEC2014和IEEE CEC2017基准函数以及六个工程问题进行了测试。
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来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
期刊介绍: Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.
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