Improved Estimation of Distribution Algorithm for Multi-objective Optimization Problems
WU Yeye
GAO Shang
Abstract：In order to improve the convergence and accuracy performance of multi-objective estimation of distribution algo?rithm,and enhance the local search capability,an improved multi-objective distribution optimization algorithm has been proposed. The basic idea of new method is using orthogonal design to initialize the population,which makes the algorithm can search in the whole feasible space,introducing the improved elitist strategy to avoid the loss of the optimal solution,while using the niche technol?ogy to maintain elite populations and prevent premature,importing genetic algorithm to evolve populations,the estimation of distri?bution algorithm makes use of in the early stage of the algorithm to search the global space quickly and the genetic algorithm is main?ly used to local optimization in the later stage. Four test functions are used in numerical experiment. The numerical results show that the proposed algorithm has a better convergence and diversity performance by compared with two other algorithms.
Keywords：multi-objective estimation of distribution algorithmgenetic algorithmorthogonal designelite strategyniche Class Number TP301.6
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1357-1363 )