Difference Selection Strategy for Solving Complex Multi-Objective Problems

Since the emergence of complex multi-objective problems in the finance and economics areas， dealing with multi-objective problems has gained increasing attention. How to improve the quality of generating solutions is the key in solving such problems. Although a number of MOEAs (multi-objective evolution algorithms) have been proposed over the last several years to solve the complex financial and economic multi-objective problems, not much effort has been made to deal with generating solutions in multi-objective optimization. Recently, we have suggested a MODEA_DACR (multi-objective difference evolution algorithm via dynamic allocation of computational resource) to improve the quality of generating solutions. The proposed algorithm uses two populations with different convergence rates to extract convergence information for the Pareto set, and then adjusts the parameter and difference evolution selection strategy according to the obtained convergence rate.In addition,based on the convergence rate of the population the proposed algorithm dynamically allocates the computational resources. The proposed algorithm is compared with two state-of-the-art algorithms, ε-MOEA and MOEA/D-DRA, on a suite of test problems with a complex Pareto set. Experimental results have shown the effectiveness of the proposed algorithm.