Optimizing Process Parameters for Ceramic Tile Manufacturing Using an Evolutionary Approach
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
Multiobjective particle swarm optimization with nondominated local and global sets
Natural Computing: an international journal
PSO and ACO in optimization problems
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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This paper introduces a proposal to extend the particle swarm optimization (PSO) to deal with constrained multiobjective optimization problems. PSO is modified by using the bidirectional searching strategy to guide each particle to search simultaneously in its neighborhood and the region where particles are distributed sparsely. The advantages of the approach are that it is easy to implement and the obtained solutions has a good distribution. It is validated using several test cases. The results show that the approach can efficiently find multiple Pareto optimal solutions. Finally, the approach is used to solve buoys-arrangement problem and the results are satisfying.