Multi-objective Estimation of Distribution Algorithm Based on Chaos Optimization and Grid Selection

To solve the poor performances of evolution speed,solution precision and distribution in the multi-objective estimation of distribution algorithm,this paper proposes a new algorithm that based on chaos optimization and grid selection strategies. The algorithm first performs initialization using chaos models to obtain better initial results. Then,a chaotic local optimization strategy is applied to get nondominating individuals in iterations,which makes the population effectively approximate the Pareto optimal front. Finally,a simple grid selection strategy is employed to keep a uniform distribution and enhance the diversity of the elite population. Experimental results on eight test problems using three performance metrics show that the new algorithm has a certain advantage compared to the most representative RM-MEDA algorithm in terms of converging to the true Pareto front and maintaining the diversity of the population,moreover,it is also much faster than RM-MEDA.