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Abstract
Optimization problems can often be simplified to the search for an optimal solution in the feasible search space. Based on the concept of simulating the act of human randomized search, a novel algorithm called seeker optimization algorithm (SOA) for real-parameter optimization is proposed in this paper. In the SOA, after given center point, search direction, search radius, and trust degree, every seeker moves to a new position (a candidate solution) from his current position based on his historical and social experiences. 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) greatly in terms of optimization quality, robustness and efficiency. At the same time, SOA greatly outperforms particle swarm optimization (PSO) in convergence speed. However, SOA needs more computation time. Simulations of designing both PID controller and IIR digital filter also show that SOA gets more satisfactory solutions with better evaluation values.