Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Computational intelligence PC tools
Computational intelligence PC tools
Efficient population utilization strategy for particle swarm optimizer
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Unified particle swarm optimization in dynamic environments
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Hybridization of differential evolution and particle swarm optimization in a new algorithm: DEPSO-2S
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
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Particle swarm optimization (PSO) is characterized by a fast convergence, which can lead the algorithms of this class to stagnate in local optima. In this paper, a variant of the standard PSO algorithm is presented, called PSO-2S, based on several initializations in different zones of the search space, using charged particles. This algorithm uses two kinds of swarms, a main one that gathers the best particles of auxiliary ones, initialized several times. The auxiliary swarms are initialized in different areas, then an electrostatic repulsion heuristic is applied in each area to increase its diversity. We analyse the performance of the proposed approach on a testbed made of unimodal and multimodal test functions with and without coordinate rotation and shift. The Lennard-Jones potential problem is also used. The proposed algorithm is compared to several other PSO algorithms on this benchmark. The obtained results show the efficiency of the proposed algorithm.