A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Swarm intelligence
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Preserving diversity in particle swarm optimisation
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
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
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
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The particle swarm optimisation (PSO) algorithm suffers from the possibility of premature convergence. This problem has historically been addressed ab intra - manipulating velocity and swarm topology - yet the judicious addition of external mechanisms has been shown to adjust search behaviour to yield significantly improved results across many problems. This paper introduces an addition to the canonical particle swarm algorithm, designed to preserve the diversity typically lost by attraction to suboptimal positions. The proposed excited PSO method stimulates exploration upon the discovery of a candidate solution by manipulating the position to which particles are attracted. It is shown to maintain a suitable degree of diversity for the duration of an experiment, as well as an ability for self-scaling. Comparisons to the canonical PSO algorithm demonstrate improved solutions in both unimodal and multimodal spaces.