An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rapid prototyping using evolutionary approaches: part 1
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Rapid prototyping using evolutionary approaches: part 2
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolutionary multi-objective optimization and decision making for selective laser sintering
Proceedings of the 12th annual conference on Genetic and evolutionary computation
On the effect of selection and archiving operators in many-objective particle swarm optimisation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Over past few years, several successful proposals for handling multi-objective optimization tasks using particle swarm optimization (PSO) have been made, such methods are popularly known as Multi-objective Particle Swarm Optimization (MOPSO). Many of these methods have focused on improving characteristics like convergence, diversity and computational times by proposing effective 'archiving' and 'guide selection' techniques. What has still been lacking is an empirical study of these proposals in a common frame-work. In this paper, an attempt to analyze these methods has been made; discussing their strengths and weaknesses. Combined effect of 'guide selection' and 'archiving' is also understood, and it turns out that there exist certain combinations which perform better in terms of convergence, or diversity, or computational times. Finally a new hybrid proposal, by coupling-dominance with Sequential Quadratic Programming (SQP) search, has been made to achieve faster and accurate convergence.