论文信息 - Dynamic fitness inheritance proportion for multi-objective particle swarm optimization

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.

Carlos A. Coello Coello | Margarita Reyes Sierra

[1] Lothar Thiele,et al. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[2] Carlos A. Coello Coello,et al. A study of fitness inheritance and approximation techniques for multi-objective particle swarm optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[3] Carlos A. Coello Coello,et al. Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance , 2005, EMO.

[4] C. Coello,et al. Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance , 2005 .

[5] Robert E. Smith,et al. Fitness inheritance in genetic algorithms , 1995, SAC '95.