Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Environmentally constrained economic dispatch using Pareto archive particle swarm optimisation
International Journal of Systems Science
On convergence of the multi-objective particle swarm optimizers
Information Sciences: an International Journal
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on 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
Multiobjective evolutionary algorithms for electric power dispatch problem
IEEE Transactions on Evolutionary Computation
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
A new fitness estimation strategy for particle swarm optimization
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Information Sciences: an International Journal
Hi-index | 0.07 |
In this paper, we propose a new bare-bones multi-objective particle swarm optimization algorithm to solve the environmental/economic dispatch problems. The algorithm has three distinctive features: a particle updating strategy which does not require tuning up control parameters; a mutation operator with action range varying over time to expand the search capability; and an approach based on particle diversity to update the global particle leaders. Several trials have been carried out on the IEEE 30-bus test system. By comparing with seven existing multi-objective optimization algorithms and three well-known multi-objective particle swarm optimization techniques, it is found that our algorithm is capable of generating excellent approximation of the true Pareto front and can be used to solve other types of multi-objective optimization problems.