Niching methods for genetic algorithms
Niching methods for genetic algorithms
Swarm intelligence
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Particle Swarm Optimization for clustering short-text corpora
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
A Particle Swarm Optimization Method for Multimodal Optimization Based on Electrostatic Interaction
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Modified particle swarm optimization for a multimodal mixed-variable laser peening process
Structural and Multidisciplinary Optimization
Hybrid immune algorithm for many optima
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization algorithm for dynamic environments
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
A hybrid of particle swarm optimization and local search for multimodal functions
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Hi-index | 0.01 |
The particle swarm optimization (PSO) algorithm is designed to find a single optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modifications can be grouped in the framework of niching. In this work, we present a new approach to niching in PSO based on clustering particles to identify niches. The neighborhood structure, on which particles rely for communication, is exploited together with the niche information to locate multiple optima in parallel. Our approach was implemented in the k-means-based PSO (kPSO), which employs the standard k-means clustering algorithm, improved with a mechanism to adaptively identify the number of clusters. kPSO proved to be a competitive solution when compared with other existing algorithms, since it showed better performance on a benchmark set of multimodal functions.