Diversity control in a multi-objective genetic algorithm

We cover an investigation on the effects of diversity control in a multiobjective genetic algorithm (MOGA). Specifically, the diversity control operator used is based on the one developed for a diversity control oriented genetic algorithm (DCGA). The performance comparison between multiobjective genetic algorithms with and without diversity control is explored where different benchmark problems with specific multiobjective characteristics are utilised. The search performance of the multiobjective genetic algorithms is determined by inspecting the closeness of solutions to the true Pareto front, the uniformity in the solution distribution and the range of solutions in the objective space. The results indicate that the use of diversity control with specific parameter settings promotes the emergence of multiobjective solutions that are close to the true Pareto optimal solutions while maintaining a uniform distribution of the solutions along the Pareto front.