A Direction based Multi-Objective Agent Genetic Algorithm

A direction based multi-objective agent genetic algorithm (DMOAGA) is proposed in this paper. In order to take advantage of the effective direction information and depth of local search to mine non-dominated solutions, the direction perturbation operator is also employed. The neighborhood non-dominated solutions are generated using tournament selection and "average distance" rule, which maintains the diversity of non-dominated solution set. In the experiments, the benchmark problems UF1 similar to UF6 and ZDT1 similar to ZDT4 are used to validate the performance of DMOAGA. We compared it with NSGA-II and DMEA in terms of generational distance (GD) and inverted generational distance (IGD). The results show that DMOAGA has a good diversity and convergence, the performances on most of benchmark problems are better than DMEA and NSGA-II.