Swarm intelligence
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Information Processing Letters
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
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
On convergence of the multi-objective particle swarm optimizers
Information Sciences: an International Journal
Discrete particle swarm optimization for TSP: theoretical results and experimental evaluations
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Inter-particle communication and search-dynamics of lbest particle swarm optimizers: An analysis
Information Sciences: an International Journal
The improved particle swarm optimization based on swarm distribution characteristics
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Beyond Standard Particle Swarm Optimisation
International Journal of Swarm Intelligence Research
Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm
International Journal of Software Science and Computational Intelligence
Particle swarm optimization almost surely finds local optima
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In this paper, particle trajectories of PSO algorithms in the first iteration are studied. We will prove that many particles leave the search space at the beginning of the optimization process when solving problems with boundary constraints in high-dimensional search spaces. Three different velocity initialization strategies will be investigated, but even initializing velocities to zero cannot prevent this particle swarm explosion. The theoretical analysis gives valuable insight into PSO in high-dimensional bounded spaces, and highlights the importance of bound handling for PSO: As many particles leave the search space in the beginning, bound handling strongly influences particle swarm behavior. Experimental investigations confirm the theoretical results.