Multi-objective evolutionary of Distribution Algorithm using kernel density estimation model

Estimation of Distribution Algorithm (EDA) is a kind of new evolutionary algorithm which updates and samples from probabilistic model in evolutionary computation. Recently it is used to solve multi-objective problems. The key is how to construct probability model suitable for real distribution and how to keep diversity of solutions. In this paper a new multi-objective evolutionary of distribution algorithm using kernel density estimation model is presented. It used kernel density estimation method to obtain probability density of samples and generate new population with stochastic universal sampling method. In order to get pareto front of multi-objective problems, fitness sharing method is used. 5 bi-objective test problems are selected to test the performance of the new algorithm. The results show that multi-objective evolutionary of distribution algorithm using kernel density estimation model has better suitable performance for test problems comparing with non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization and multi-objective estimation of distribution algorithm.