混乱的
趋同(经济学)
水准点(测量)
初始化
计算机科学
数学优化
算法
非线性系统
粒子群优化
人口
萤火虫算法
数学
人工智能
地理
人口学
程序设计语言
经济
社会学
物理
量子力学
经济增长
大地测量学
作者
Hangqi Ding,Zhi‐Yong Wu,Luchen Zhao
摘要
Summary The whale optimization algorithm (WOA) is a new biological meta‐heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth‐flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed.
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