A NEW APPROACH ON MOPSO USING SIMULATED ANNEALING & GENETIC ALGORITHM CONCEPTS

Optimization plays an important role in many areas of science, management, economics and engineering. This paper presents a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm which has been optimized using genetic algorithm that has new components: selection using genetic algorithm, crossover and improved PSO. The genetic algorithm optimizes the results by applying various operators of genetic. The selection process is also different which somehow improves the results. Besides, crossover and mutation adopted in the proposed algorithm also contributes to the betterment of the results. The performance of the proposed Simulated Annealing based MOPSO algorithm was compared with four popular multi-objective algorithms in solving standard test functions. Their performance measures were mainly calculated on hypervolume and proposed algorithm was generally better from previous.