Algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Parameter Selection in Particle Swarm Optimization
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
Containing particles inside niches when optimizing multimodal functions
SAICSIT '05 Proceedings of the 2005 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
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
A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
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Several techniques have been proposed to extend the particle swarm optimization (PSO) paradigm so that multiple optima can be located and maintained within a convoluted search space. A significant number of these implementations are subswarm-based, that is, portions of the swarm are optimized separately. Niches are formed to contain these subswarms, a process that often requires user-specified parameters. The proposed technique, known as the vector-based PSO, uses a novel approach to locate and maintain niches by using additional vector operations to determine niche boundaries. As the standard PSO uses weighted vector combinations to update particle positions and velocities, the niching technique builds upon existing knowledge of the particle swarm. Once niche boundaries have been calculated, the swarm can be organized into subswarms without prior knowledge of the number of niches and their corresponding niche radii. This paper presents the vector-based PSO with emphasis on its underlying principles. Results for a number of functions with different characteristics are reported and discussed. The performance of the vector-based PSO is also compared to two other niching techniques for particle swarm optimization.