Progressive Stepping Based Non Dominated Sorting Genetic Algorithm for Multi Objective Optimization
This paper demonstrates two approaches to achieve faster convergence and a better spread of Pareto solutions in fewer numbers of generations, compared to a few existing algorithms, including NSGA II and SPEA2 to solve multi objective optimization problems MOP's. Two algorithms are proposed based on progressive stepping mechanism, which is obtained by the hybridization of existing Non dominated Sorting Genetic Algorithm II NSGA II with novel guided search schemes, and modified chromosome selection and replacement mechanisms. Progressive Stepping Non dominated Sorting based on Local search PSNS L controls the step size, and Progressive Stepping Non dominated Sorting based on Utopia point PSNS U method controls the number of divisions to generate better chromosomes in each generation to achieve faster convergence. Four multi objective evolutionary algorithms EA's are compared for different benchmark functions and PSNS outperforms them in most cases based on various performance metric values. Finally a mechanical design problem has been solved with PSNS algorithms.
Int. J. Appl. Evol. Comput.