Improved NSGA-II Algorithm for Optimization of Constrained Functions

Abstract

Optimization of Constrained Functions have been a research focus in multi-objective optimization problems (MOP). Based on the technologies from NSGA-II such as non-dominated sorting, elitist strategy and niche technique, this paper proposes an improved NSGA-II algorithm for Optimization of Constrained Functions. In the improved algorithm, a partial order relation and the crossover operate by Cauchy Distribution is set up. Then according to the partial order relation, the individuals are sorted for generating the non-dominated individuals. Moreover, to enhance the evolution’s ability, some individuals are evolved in the same generation and the crossover operate by Cauchy Distribution is adopted. In addition, non-dominated individuals generated in each generation are archived to Pareto set filter to reserve all individuals with good characteristic generated in the evolving process. Finally, some Benchmark functions are used to test the algorithm performance. Test result shows the availability and the efficiency of the algorithm.