An improved Non-dominated Sorting Genetic Algorithm-II (ANSGA-II) with adaptable parameters

Multi-Objective Evolutionary Algorithms (MOEAs) are not easy to use because they require parameter tunings to achieve good solutions and performance for an arbitrary complex problem. This paper introduces a MOEA with adaptive population size, self-adaptive crossover and self-adaptive mutation for automating the process of adjusting parameter values to make the MOEA simple to use. The new MOEA is built on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and named as Adaptable NSGA-II (ANSGA-II). Simulation results on 13 multi-objective problems demonstrate that the ANSGA-II out-performs the NSGA-II in terms of finding diverse non-dominated solutions and converging to the true Pareto-optimal front.