No abstract

A novel heuristic search algorithm called seeker optimization 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 empirical 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 comparison 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.

No abstract

No abstract

Abstract.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.