Optimization algorithm with stochastic focusing search
Weibo Wang · Control theory & applications · 2009
A novel optimization algorithm with stochastic focusing search(SFS) is proposed. This algorithm is a swarmintelligence algorithm, which imitates the random action in human searching behaviors. The algorithm performance is studied by using a set of typical complex functions, and is compared with that of the differential evolution(DE) algorithm and the comprehensive learning-particle-swarm-optimizer(CLPSO) algorithm. The simulation results show that SFS solves most of the benchmark problems and can be considered a promising candidate of search algorithms when the existing algorithms have difficulties in solving some problems.