Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Studying solutions of traveling salesman problem with hybrid particle swarm optimization
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Scalability of the vector-based particle swarm optimizer
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A novel particle swarm niching technique based on extensive vector operations
Natural Computing: an international journal
Effect of particle initialization on the performance of particle swarm niching algorithms
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Niching for dynamic environments using particle swarm optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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Traditionally particle swarm optimization was employed to locate a single optimal solution in a search space. The strategy can be adapted in niching algorithms to find multiple optimal solutions in a problem domain. Initially good candidate solutions must be found to serve as nuclei around which subswarms can be optimized to converge on multiple optimal solutions. Several strategies have been developed to address the problem. Algorithms to accomplish this should not depend on prior knowledge of the objective function to find starting points and estimate the niche boundaries.In previous work the authors introduced a vector-based approach to identify and demarcate the initial niches. Niches were originally optimized sequentially and later in parallel. A critical objective entailed the minimization of tunable parameters and problem dependence. This paper presents an enhanced parallel vector-based particle swarm optimizer where niches are identified using vector operations and subswarms optimized in parallel without using a niche radius.