Su Hyun Jang , Byung Joo Yoon

The KIPS Transactions:PartB , Vol. 11, No. 7, pp. 841-848, Dec. 2004
10.3745/KIPSTB.2004.11.7.841,   PDF Download:

Abstract

Evolutionary algorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uniformly distributed onto pareto optimal front. In this paper, we propose an evolutionary algorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms published. Seed individual means a solution which is not lacated in the crowded region on pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed algorithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algorithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-model problem.

Statistics
Cite this article