Incorporation Of A Decision Space Diversity Maintenance Mechanism Into Evolutionary Multi-objective Optimization Algorithms

In recent years,multi-modal multi-objective optimization has received extensive attention.A lot of scholars have researched it.In a multi-modal multi-objective optimization problem,all Pareto optimal solutions that have the same objective vector but distribute differently in the decision space are required to be found.This thesis studies the performance of several representative evolutionary multiobjective optimization algorithms on a multi-modal multi-objective optimization problem.The experimental results suggest that since there lacks the mechanism to maintain diversity,the diversity of solutions in the decision space becomes worse and worse during algorithm execution.In order to handle this issue,we proposes two mechanisms to maintain the diversity of solutions in the decision space.The main contributions of this thesis are as follows:(1)We propose a subpopulation searching method to solve multi-modal multiobjective optimization problems.The method divides the population into several subpopulations.The environmental selection and reproduction procedures perform independently in each subpopulation.Two metrics are designed to make solutions form niches in the decision space.The first metric is the distance between a solution and the center of the subpopulation where it exists.The second metric is the distance between a solution and the center of another nearest subpopulation.The two metrics make the subpopulations away from each other,thus to form niches in the decision space.We apply the subpopulation searching method to SPEA2 and IBEA.The experimental results show that the subpopulation searching method can significantly improve the decision space diversity for the two algorithms.(2)We propose the neighborhood anchor method to solve multi-modal multiobjective optimization problems.In each generation,the method finds 9)nearest neighbors in the archive for each solution in the population.Then,the method selects the worst neighbor based on fitness function values.If the fitness function value of the solution is better than the fitness function value of its worst neighbor ,solution will replace the worst neighbor .The offspring solutions are generated by the solutions in the archive.We apply the neighborhood anchor method to SPEA2 and IBEA.The experimental results show that the neighborhood anchor method can also improve the decision space diversity for the two algorithms.