Swarm intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multi-objective Optimisation Based on Relation Favour
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Quantifying the effects of objective space dimension in evolutionary multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
About selecting the personal best in multi-objective particle swarm optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Online Objective Reduction to Deal with Many-Objective Problems
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Iterated multi-swarm: a multi-swarm algorithm based on archiving methods
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Optimization problems with many objectives open new issues for multi-objective optimization algorithms and particularly Particle Swarm Optimization. Many of the existing algorithms are able to solve problems of low number of objectives, but as soon as we increase the number of objectives, their performances get even worse than random search methods. This paper gives an overview on Multi-objective Particle Swarm Optimization when having many objectives and parameters. Furthermore, two new variants of MOPSO are proposed which are based on ranking of the non-dominated solutions. The proposed distance based ranking in MOPSO improves the quality of the solutions for even very large objective and parameter spaces. The quality of the new proposed MOPSO methods has been tested and compared to the random search and NSGA-II methods. The tests cover 3 to 20 objectives and 20 to 100 parameters.