Multiple comparison procedures
Multiple comparison procedures
Swarm intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
International Journal of Intelligent Systems
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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 MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
On convergence of the multi-objective particle swarm optimizers
Information Sciences: an International Journal
VarMOPSO: multi-objective particle swarm optimization with variable population size
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Engineering Applications of Artificial Intelligence
Using MOPSO to solve multiobjective bilevel linear problems
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
A multi-objective particle swarm optimizer enhanced with a differential evolution scheme
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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Particle Swarm Optimization (PSO) has received increasing attention in the optimization research community since its first appearance in the mid-1990s. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOPSOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which MOPSO version shows the best performance. In this paper, we use a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) with the aim of analyzing the search capabilities of six representative state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO, AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new MOPSO algorithm, called SMPSO, characterized by including a velocity constraint mechanism, obtaining promising results where the rest perform inadequately.