Abstract:

A novel optimization algorithm called stochastic focusing search (SFS) for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of human randomized searching, and the human searching behaviors. The algorithm’s performance is studied using a challenging set of typically complex functions with comparison of differential evolution (DE) and three modified particle swarm optimization (PSO) algorithms, and the simulation results show that SFS is competitive to solve most parts of the benchmark problems and will become a promising candidate of search algorithms especially when the existing algorithms have some difficulties in solving certain problems.