Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Optimization using particle swarms with near neighbor interactions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Neighborhood re-structuring in particle swarm optimization
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
This paper introduces a new dynamic neighborhood network for particle swarm optimization. In Club-based Particle Swarm Optimization (C-PSO) algorithm, each particle initially joins a default number of social groups (clubs). Each particle is affected by its own experience and the experience of the best performing member of the social groups it is a member of. In the proposed Adaptive membership C-PSO (AMC-PSO), a time varying default Membership is introduced. This modification enables the particles to explore the space based on their own experience in the first stage, and to intensify the connections of the social network in later stages to avoid premature convergence. This proposed dynamic neighborhood algorithm is compared with other PSO algorithms having both static and dynamic neighborhood topologies on a set of classic benchmark problems. The results showed superior performance for AMC-PSO regarding its ability to escape from local optima, while its speed of convergence is comparable to other algorithms.