An Improved Ranking Scheme for Selection of Parents in Multi-Objective Genetic Algorithm

Abstract

Among the three genetic operator selection, crossover and mutation, selection operator is very important. Selection operator has got the force that may pull the search to a narrow area of search space or it may lend the algorithm to search the entire search space. This work focuses attention on the selection stage of multi-objective Genetic algorithm (MOGA) used for solving multi-objective optimization problems. Here we propose an improved selection scheme along with summation of normalized objective value based sorting. The algorithm is tested on test problems of CEC09 competition. The proposed algorithm SNOVMOGA (Summation of Normalized Objective Value based Multi-objective Genetic Algorithm) has shown either comparable or good performance on few unconstrained test problems. The goal of performance improvement of the real-coded multi-objective genetic algorithm has been achieved to some extent in this work.