IMOGA/SOM: An intelligent multi-objective genetic algorithm using self organizing map

Abstract

Multi-objective Genetic Algorithms (MOGAs) are probabilistic search techniques and provide solutions of multi-objective optimization problems. When MOGA reaches near optimal regions, it may face problem in convergence due to its probabilistic nature. MOGA does not pay attention on the neighbourhood of the current population which makes the convergence slow. This scenario may also lead to premature convergence. To overcome this problem, we propose an Intelligent Multiobjective Genetic Algorithm using Self Organizing Map (IMOGA/SOM). The proposed algorithm uses the neighbourhood property of SOM. SOM is trained by the solutions generated by MOGA. SOM performs competition and cooperation among its neurons for better convergence. We have compared the results of the proposed algorithm with two existing algorithms NSGA-II and SOM-Based Multi Objective Genetic Algorithm (SBMOGA). Empirical results demonstrate the superiority of the proposed algorithm IMOGA/SOM.