Abstract
The objectives of the Multi-Objective Evolutionary Algorithms (MOEA) are summarized as follows: (1) To find the pareto optimal solutions, (2) To find the pareto optimal solutions as diverse as possible. To achieve these objectives by the PSO for the single objective problems, we propose how to define the g-best in the swarm without introducing some new parameters. Additionally, some particles among the non- inferior solutions are also selected as the g-best of the inferior solutions to find the pareto optimal solutions. The absolute distance in the objective space is utilized to select the g-best of the inferior solutions. We also show the geometric interpretation about the movement of particles. The validity of proposed approach is examined through typical numerical examples.
