Modified Crayfish Optimization Algorithm with Adaptive Spiral Elite Greedy Opposition-based Learning and Search-hide Strategy for Global Optimization

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL
Guanghui Li, Taihua Zhang, Chieh-Yuan Tsai, Yao Lu, Jun Yang, Liguo Yao
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引用次数: 0

Abstract

Crayfish optimization algorithm (COA) is a novel, bionic, metaheuristic algorithm with high convergence speed and solution accuracy. However, in some complex optimization problems and real application scenarios, the performance of COA is not satisfactory. In order to overcome the challenges encountered by COA, such as being stuck in the local optimal and insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning (ASEG-OBL), competition-elimination, and chaos mutation. To evaluate the convergence accuracy, speed, and robustness of the modified crayfish optimization algorithm (MCOA), some simulation comparison experiments of 10 algorithms are conducted. Experimental results show that the MCOA achieved the minor Friedman test (FT) value in 23 test functions, CEC2014, and CEC2020, and achieved average superiority rates of 80.97%, 72.59%, and 71.11% in the Wilcoxon rank sum test (WT) respectively. In addition, MCOA shows high applicability and progressiveness in five engineering problems in actual industrial field. Moreover, MCOA achieved 80% and 100% superiority rate against COA on CEC2020 and the fixed-dimension function of 23 benchmark test functions. Finally, MCOA owns better convergence and population diversity.
基于自适应螺旋精英对立学习和搜索隐藏策略的全局优化修正小龙虾优化算法
小龙虾优化算法(COA)是一种新颖的仿生元启发式算法,具有收敛速度快、求解精度高的特点。然而,在一些复杂的优化问题和实际应用场景中,COA 的性能并不尽如人意。为了克服 COA 遇到的难题,如陷入局部最优和搜索范围不足等,本文提出了四种改进策略:搜索隐藏、自适应螺旋精英贪婪对立学习(ASEG-OBL)、竞争消除和混沌突变。为了评估改进后的小龙虾优化算法(MCOA)的收敛精度、速度和鲁棒性,本文对 10 种算法进行了仿真对比实验。实验结果表明,MCOA 在 23 个测试函数、CEC2014 和 CEC2020 中取得了较小的 Friedman 检验(FT)值,在 Wilcoxon 秩和检验(WT)中分别取得了 80.97%、72.59% 和 71.11% 的平均优越率。此外,MCOA 在实际工业领域的五个工程问题中表现出较高的适用性和进步性。此外,在 CEC2020 和 23 个基准测试函数的定维函数上,MCOA 与 COA 相比分别取得了 80% 和 100% 的优越性。最后,MCOA 还具有更好的收敛性和群体多样性。
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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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