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Published in:
2007 | OriginalPaper | Chapter
Seeker Optimization Algorithm
Authors : Chaohua Dai, Yunfang Zhu, Weirong Chen
Published in: Computational Intelligence and Security
Publisher: Springer Berlin Heidelberg
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A novel swarm intelligence paradigm called seeker optimization algorithm (SOA) for the real-parameter optimization is proposed in this paper. The SOA is based on the concept of simulating the act of humans’ intelligent search with their memory, experience, and uncertainty reasoning. In this sense, the individual of this population is called seeker or searcher just from which the new algorithm’ name is derived. After given start point, search direction, search radius, and trust degree, every seeker moves to a new position (next solution) based on his social learning, cognitive learning, and uncertainty reasoning. The algorithm’s performance was studied using several typically complex functions. In almost all cases studied, SOA is superior to continuous genetic algorithm (GA) and particle swarm optimization (PSO) in all optimization quality, robustness and efficiency.