Dynamic fitness inheritance proportion for multi-objective particle swarm optimization

In this paper, we propose a dynamic mechanism to vary the probability by which fitness inheritance is applied throughout the run of a multi-objective particle swarm optimizer, in order to obtain a greater reduction in computational cost (than the obtained with a fixed probability), without dramatically affecting the quality of the results. The results obtained show that it is possible to reduce the computational cost by 32% without affecting the quality of the obtained Pareto front. Categories and Subject Descriptors: I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Margarita Reyes Sierra, Carlos A. Coello Coello
• Keywords: Computational Cost, Dynamic Mechanism, GECCO 2006, Optimization, Particle Swarm Optimizer