Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary multiobjective optimization
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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The particle swarm optimization algorithm has been used for solving multi-objective optimization problems in last decade. This algorithm has a capacity of fast convergence; however its exploratory capability needs to be enriched. An alternative method of overcoming this disadvantage is to add mutation operator(s) into particle swarm optimization algorithms. Since the single-point mutation is good at global exploration, in this paper a new coevolutionary algorithm is proposed, which combines single-point mutation and particle swarm optimization together. The two operators are cooperated under the framework of mixed strategy evolutionary algorithms. The proposed algorithm is validated on a benchmark test set, and is compared with classical multi-objective optimization evolutionary algorithms such as NSGA2, SPEA2 and CMOPSO. Simulation results show that the new algorithm does not only guarantee its performance in terms of fast convergence and uniform distribution, but also have the advantages of stability and robustness.