Seeker Optimization Algorithm

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