Computational intelligence PC tools
Computational intelligence PC tools
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
Social interaction in particle swarm optimization, the ranked FIPS, and adaptive multi-swarms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The singly-linked ring topology for the particle swarm optimization algorithm
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
An exploration of topologies and communication in large particle swarms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A complex neighborhood based particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle swarm optimizer with self-adjusting neighborhoods
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Neighborhood re-structuring in particle swarm optimization
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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
A spatial random-meaningful neighbourhood topology in pso for edge detection in noisy images
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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Particle swarm optimisation (PSO) is an intelligent random search algorithm, and the key to success is to effectively balance between the exploration of the solution space in the early stages and the exploitation of the solution space in the late stages. This paper presents a new dynamic topology called "gradually increasing directed neighbourhoods (GIDN)" that provides an effective way to balance between exploration and exploitation in the entire iteration process. In our model, each particle begins with a small number of connections and there are many small isolated swarms that improve the exploration ability. At each iteration, we gradually add a number of new connections between particles which improves the ability of exploitation gradually. Furthermore, these connections among particles are created randomly and have directions. We formalise this topology using random graph representations. Experiments are conducted on 31 benchmark test functions to validate our proposed topology. The results show that the PSO with GIDN performs much better than a number of the state of the art algorithms on almost all of the 31 functions.