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.
fitness inheritance particle swarm optimization multi-objective optimization