Swarm intelligence
Evolutionary Design by Computers with CDrom
Evolutionary Design by Computers with CDrom
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Optimization of power allocation for interference cancellation with particle swarm optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
A simple strategy to maintain diversity and reduce crowding in particle swarm optimization
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Normalized population diversity in particle swarm optimization
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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Premature convergence happens in Particle Swarm Optimization PSO for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get "stuck in" the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm's ability of exploration and exploitation. Through the population diversity measurement, useful search information can be obtained. PSO with a different topology structure and a different boundary constraints handling strategy will have a different impact on particles' exploration and exploitation ability. In this paper, the phenomenon of particles gets "stuck in" the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques, and analyze the impact of these setting on the algorithm's ability of exploration and exploitation. From these experimental studies, an algorithm's ability of exploration and exploitation can be observed and the search information obtained; therefore, more effective algorithms can be designed to solve problems.