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

作　　者：Chaohua Dai Weirong Chen Yonghua Song Yunfang Zhu 

机构地区：[1]School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, R R. China [2]Department of Electronic Engineering, Tsinghua University, Beijing 100084, R R. China [3]Department of Computer and Communication Engineering, E'mei Campus, Southwest Jiaotong University, E'mei 614202, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2010年第2期300-311,共12页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(60870004)

摘　　要：A novel heuristic search algorithm called seeker op- timization 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 empir- ical 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 compari- son 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.A novel heuristic search algorithm called seeker op- timization 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 empir- ical 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 compari- son 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.

关 键 词：swarm intelligence global optimization human searching behaviors seeker optimization algorithm. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O242.23[自动化与计算机技术—控制科学与工程]