Diversity preservation with hybrid recombination for evolutionary multiobjective optimization
Sen Bong Gee, Kay Chen Tan · 2014
Convergence and diversity are two crucial issues in evolutionary multiobjective optimization. To enhance the diversity property of Multiobjective Evolutionary Algorithm (MOEA), a novel selection method is implemented on decomposition-based MOEA (MOEA/D). The selection method incorporates the concept of maximum diversity loss, which quantifies the diversity loss of each individual in every generation. By monitoring tolerance of the diversity loss, the diversity of the solutions in each generation can be preserved. To further enhance the algorithm's search ability, a new hybrid recombination strategy is implemented by taking the advantage of different recombination operators. In terms of Inverted Generational Distance (IGD), the experiment results shown that the proposed algorithm, namely DHRS-MOEA/D, performed significantly better than many state-of-the-art MOEAs in most of the CEC-09 and WFG test problems.