Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization

被引：61
作者：
Dai, Chaohua [1 ]
Chen, Weirong [1 ]
Song, Yonghua [2 ]
Zhu, Yunfang [3 ]
机构：
[1] SW Jiaotong Univ, Sch Elect Engn, Chengdu 610031, Peoples R China
[2] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[3] SW Jiaotong Univ, Dept Comp & Commun Engn, Emei 614202, Peoples R China
来源：
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS | 2010年 / 21卷 / 02期
基金：
中国国家自然科学基金;
关键词：
swarm intelligence; global optimization; human searching behaviors; seeker optimization algorithm; DIFFERENTIAL EVOLUTION; SWARM;
D O I：
10.3969/j.issn.1004-4132.2010.02.021
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel heuristic search algorithm called seeker optimization algorithm (SOA) is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empirical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in comparison to differential evolution (DE) and three modified particle swarm optimization (PSO) algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.
引用
收藏
页码：300 / 311
页数：12
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