Whale Swarm Algorithm for Function Optimization

被引：29

作者：

Zeng, Bing ^{[1
]}

Gao, Liang ^{[1
]}

Li, Xinyu ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China

来源：

INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT I | 2017年 / 10361卷

基金：

中国国家自然科学基金;

关键词：

Whale Swarm Algorithm; Ultrasound; Nature-inspired; Metaheuristic; Function optimization; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; ROUTING ALGORITHM;

D O I：

10.1007/978-3-319-63309-1_55

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper proposes a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired from the whales' behavior of communicating with each other via ultrasound for hunting. The proposed Whale Swarm Algorithm is compared with several popular metaheuristic algorithms on comprehensive performance metrics. According to the experimental results, Whale Swarm Algorithm has a quite competitive performance when compared with other algorithms.

引用

页码：624 / 639

页数：16

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