In the past two decades, multi-objective optimization has attracted increasing
interests in the evolutionary computation community, and a variety
of multi-objective optimization algorithms have been proposed on the
basis of different population based meta-heuristics, where the family of
multi-objective particle swarm optimization is among the most representative
ones. While the performance of most existing multi-objective particle
swarm optimization algorithms largely depends on the global or personal
best particles stored in an external archive, in this paper, we propose
a competitive mechanism based multi-objective particle swarm optimizer,
where the particles are updated on the basis of the pairwise competitions
performed in the current swarm at each generation. The performance
of the proposed competitive multi-objective particle swarm optimizer is
verified by benchmark comparisons with several state-of-the-art multiobjective
optimizers, including three multi-objective particle swarm optimization
algorithms and three multi-objective evolutionary algorithms.
Experimental results demonstrate the promising performance of the proposed
algorithm in terms of both optimization quality and convergence
speed