Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

The main goal of this paper is to investigate the effect of the multiple search operators with adaptive selection strategy and to develop hybrid version of non-dominated sorting genetic algorithm (HNSGA) for solving recently developed complicated multi-objective optimization test suit for multi-objective evolutionary algorithms (MOEAs) competition in the special session of the congress on evolutionary computing held at Norway in 2009 (CEC'09). The Inverted generational distance (IGD) has been used performance indicator to establish valuable comparison between the suggested algorithm and NSGA-II as shown in the figure below. A set of Pareto optimal solutions with smaller is the IGD values confirm that approximated Pareto front (PF) will cover whole part of true PF in term of proximity and diversity. The average IGD-metric values evolution obtained by HNSGA versus NSGA-II for UF1-UF5 within allowable resources of 300,000 function evaluations. 104