Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
This paper proposes a dynamic sub-swarms multi-objective particle swarm optimization algorithm (DSMOPSO). Based on solution distribution of multi-objective optimization problems, it separates particles into multi subswarms, each of which adopts an improved clustering archiving technique, and operates PSO in a comparably independent way. Clustering eventually enhances the distribution quality of solutions. The selection of the closest particle to the gbest from archiving set and the developed pbest select mechanism increase the choice pressure. In the meantime, the dynamic set particle inertial weight, namely, particle inertial weight being relevant to the number of dominating particles, effectively keeps the balance between the global search in the preliminary stage and the local search in the later stage. Experiments show that this strategy yields good convergence and strong capacity to conserve the distribution of solutions, specially for the problems with non-continuous Pareto-optimal front.