Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Swarm intelligence
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
The gregarious particle swarm optimizer (G-PSO)
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Restarting multi-type particle swarm optimization using an adaptive selection of particle type
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
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The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSOPSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO.