Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Erratum: a species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A sequential niche technique for multimodal function optimization
Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
The local minima-free condition of feedforward neural networks forouter-supervised learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Niche particle swarm optimization for neural network ensembles
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Multimodal optimization using a bi-objective evolutionary algorithm
Evolutionary Computation
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This paper proposed a modified algorithm, sequential niching particle swarm optimization (SNPSO), for the attempt to get multiple maxima of multimodal function. Based on the sequential niching technique, our proposed SNPSO algorithm can divide a whole swarm into several sub-swarms, which can detect possible optimal solutions in multimodal problems sequentially. Moreover, for the purpose of determining sub-swarm's launch criteria, we adopted a new PSO space convergence rate (SCR), in which each sub-swarm can search possible local optimal solution recurrently until the iteration criteria is reached. Meanwhile, in order to encourage every sub-swarm flying to a new place in search space, the algorithm modified the raw fitness function of the new launched sub-swarm. Finally, the experimental results show that the SNPSO algorithm is more effective and efficient than the SNGA algorithm.