Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Guest Editorial Special Issue on Particle Swarm Optimization
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Two-level of nondominated solutions approach to multiobjective particle swarm optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Topology optimization of compliant mechanism using multi-objective particle swarm optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A new memetic strategy for the numerical treatment of multi-objective optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Distance Based Ranking in Many-Objective Particle Swarm Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Empirical comparison of MOPSO methods: guide selection and diversity preservation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
EMOPSO: a multi-objective particle swarm optimizer with emphasis on efficiency
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
HCS: a new local search strategy for memetic multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Multiobjective particle swarm optimization with nondominated local and global sets
Natural Computing: an international journal
Preference-based multi-objective particle swarm optimization using desirabilities
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Multiobjective optimization for nurse scheduling
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Expert Systems with Applications: An International Journal
International Journal of Swarm Intelligence Research
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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In particle swarm optimization, a particle's movement is usually guided by two solutions: the swarm's global best and the particle's personal best. Selecting these guides in the case of multiple objectives is not straightforward. In this paper, we investigate the influence of the personal best particles in Multi-Objective Particle Swarm Optimization. We show that selecting a proper personal guide has a significant impact on algorithm performance. We propose a new idea of allowing each particle to memorize all non-dominated personal best particles it has encountered. This means that if the updated personal best position be indifferent to the old one, we keep both in the personal archive. Also we propose several strategies to select a personal best particle from the personal archive. These methods are empirically compared on some standard test problems.