Swarm intelligence
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A review of adaptive population sizing schemes in genetic algorithms
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Variable Size Population in Parallel Evolutionary Algorithms
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Revisiting evolutionary algorithms with on-the-fly population size adjustment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Self-regulated population size in evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Incremental Particle Swarm-Guided Local Search for Continuous Optimization
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
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In this paper, we investigate the benefits of dynamically varying the population size in the Particle Swarm Optimization (PSO) model. For this purpose, two well-known population resizing techniques, originally developed for Genetic Algorithms (GAs), were adapted to the PSO context, giving birth to the APPSO and PRoFIPSO variants. Contrary to some previous work that has indicated that the PSO model is not sensitive to the population dimension, the simulation results we have obtained over some benchmark numerical optimization problems suggest that the dynamic variation of the number of particles may be instrumental for bringing about performance improvements in long-term runs, mainly when considering high-dimensional problem instances. In general, the novel PSO variants have compared more favorably to their GA counterparts in targeting the optimal solutions. However, regarding PRoFIPSO specifically, the price to be paid in terms of resources used to reach the optimum point is as a rule very high.