A Differential Evolution Algorithm for Multi-objective Mixed-Variable Optimization Problems
Abstract: Multi-objective mixed-variable optimization problems (MO-MVOPs) are common and complex practical design optimization problems. MO-MVOPs often include multiple complex functions, constraints and mixed types of decision variables. Compared with single objective mixed-variable optimization problems (MVOPs), the decision space of MO-MVOPs presents more complex spatial distribution features. These features of MO-MVOPs make solving such problems face a big challenge. In this paper, fundamental advancements are made to MCDEmv which is previously proposed for single objective MVOPs. This improved version can solve MO-MVOPs, which can be named as MO-MCDEmv. In MO-MCDEmv, the best solution in the population is no longer the solution with the best fitness value, but a random solution in the first rank after executing the fast non-dominated sorting approach in NSGA-II. The generation of offsprings is generated by using the selection operator in NSGA-II. In addition, the local search in MCDEmv is utilized to improve the parents. The quality of the newly generated individual depends on the dominance relationship between itself and its parent. The experimental results of two actual MO-MVOPs are obtained by using two advanced multi-objective algorithms, i.e., CMOEA/D and NSGA-II, and the proposed MO-MCDEmv. The experimental results show that the MO-MCDEmv has better performance than the two advanced multi-objective algorithms.
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