Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
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
The maximin fitness function: multi-objective city and regional planning
EMO'03 Proceedings of the 2nd 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
Handling multiple objectives with particle swarm optimization
IEEE Transactions on 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
Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolutionary multi-objective optimization and decision making for selective laser sintering
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Improving the efficiency of -dominance based grids
Information Sciences: an International Journal
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
Computational Optimization and Applications
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In this paper, we review several proposals for guide selection in Multi-Objective Particle Swarm Optimization (MOPSO) and compare them with each other in terms of convergence, diversity and computational times. The new proposals made for guide selection, both personal best ('pbest') and global best ('gbest'), are found to be extremely effective and perform well compared to the already existing methods. The combination of selection methods for choosing 'gbest' and 'pbest' is also studied and it turns out that there exist certain combinations which yield an overall superior performance outperforming the others on the tested benchmark problems. Furthermore, two new proposals namely velocity trigger (as a substitute for "turbulence operator") and a new scheme of boundary handling is made.