Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Extending particle swarm optimisation via genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma
IEEE Transactions on Evolutionary Computation
OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An improved particle swarm optimization with an adaptive updating mechanism
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
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The tendency to converge prematurely is a main limitation which affects the performacne of evolutionary computation algorithm, including particle swarm optimization (PSO). To overcome the limitation, we propose an extended PSO algorithm, called re-diversified particle swarm optimization (RDPSO). When population diversity is small, i.e., particles's velocity approches zero and the algorithm stagnates, a restart approach called diversification mechanism begins to work, which disperses particles and lets them leave bad positions. Based on the diversity calculated by the particles' current positions, the algorithm decides when to start the diversification mechanism and when to return the usual PSO. We testify the performance of the proposed algorithm on a 10 benchmark functions and provide comparisons with 4 classical PSO variants. The numerical experiment results show that the RDPSO has superior performace in global optimization, especially for those complex multimodal functions whose solution is difficult to be found by the other tested algorithm.