Seeker Optimization Algorithm

Author

Dai, Chaohua ; Chen, Weirong ; Zhu, Yunfang

Author_Institution

Sch. of Electr. Eng., Southwest Jiaotong Univ., Chengdu

Volume

1

fYear

2006

fDate

Nov. 2006

Firstpage

225

Lastpage

229

Abstract

A novel algorithm called seeker optimization algorithm (SOA) for the real-parameter optimization is proposed in this paper. SOA is based on the concept of simulating the act of human randomized search. In the SOA, after given center point, search direction, search radius, and trust degree, every seeker moves to a new position (next solution) from his current position based on his historical and social experience. In this process, the update formula is like Y-conditional cloud generator. The algorithm´s performance was studied using several typically complex functions. In all cases studied, SOA is superior to continuous genetic algorithm (CGA) and particle swarm optimization (PSO) greatly in terms of optimization quality, robustness and efficiency

Keywords

genetic algorithms; particle swarm optimisation; search problems; Y-conditional cloud generator; continuous genetic algorithm; human randomized search; particle swarm optimization; real-parameter optimization; seeker optimization algorithm; Chaos; Clouds; Computer crashes; Genetic algorithms; Humans; Layout; Particle swarm optimization; Robustness; Semiconductor optical amplifiers; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2006 International Conference on

Conference_Location

Guangzhou

Print_ISBN

1-4244-0605-6

Electronic_ISBN

1-4244-0605-6

Type

conf

DOI

10.1109/ICCIAS.2006.294126

Filename

4072079