Using Dominated Solutions to the Uniformity of Non-dominated Solution Distributions in NSGA-II

Kina Yokoyama, Yuji Sato


This paper proposes a method for improving the diversity of the Pareto front in a fast elitist non-dominated sorting genetic algorithm (NSGA-II), which is an evolutionary multi-objective optimization algorithm. Conventional NSGA-II has excellent convergence to the Pareto front, but it has been reported that for some test cases, it does not produce a more diverse solution distribution than the strength Pareto evolutionary algorithm 2 (SPEA2). To avoid this problem, we propose a method that stores an archive of dominated solutions that may be effective in improving diversity in the conventional search process when used for genetic operations. We experimentally compare this approach with the conventional method on the typical multi-objective test problems ZDT1, ZDT2, and ZDT3. By evaluating the performance based on Pareto front diagrams and hypervolume values, we show that the proposed method is effective at improving the diversity at both ends of Pareto optimal front and the uniformity of the solution distribution.


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