Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
A Novel Method for Finding Good Local Guides in Multi-objective Particle Swarm Optimization
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
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
| Hi-index | 0.02 |
The Elitism, which is the mechanism to incorporate external useful solutions in MOEA, is popular technology. One of the focus of research about elitism is how to maintain and select the global guide in order to keep the results of algorithm convergence and diversity. In this paper, a novel method to maintain elitism archive and select global guide is proposed, which divide the non-dominated solutions in the elitism archive to two kind:convergence solution and diversity solution and provides the particle angle division to manage it. The MOPSO algorithm based on the new method is compared with other multi-objective evolutionary algorithm on three complicated benchmark multi-objective function optimization problems. It is shown from the results that the Pareto front obtained with the MOPSO has good convergence and diversity.