Steady-State Evolutionary Algorithm for Multimodal Function Global Optimization

This paper presents a two-phase steady-state evolutionary algorithm (TSEA) for solving function optimization containing multiple global optima. The algorithm includes two phases: firstly,steady-state evolution algorithm is used to get sub-optimal solutions in the global search,it enables individual to draw closer to each optimal solution,thus population is divided into subpopulations automatically after the global search.Secondly,local search is carried in the neighborhood of the best individual of each subpopulation to obtain precise solutions. Comparing with other algorithms, it has the following advantages. (1) It designs a new multi-parent crossover operator with strong direction which can accelerate the convergence.(2) A novel replacement strategy is proposed to maintain the diversity of population.This strategy is very simple and effective with little computational cost.(3) Proposed algorithm needs no additional control parameter which depends on a special problem.The experiment results show that TSEA is very efficient for the optimization of multi-modal functions.