Computational intelligence PC tools
Computational intelligence PC tools
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Natural Computing: an international journal
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Cognitive radio adaptation using particle swarm optimization
Wireless Communications & Mobile Computing
A novel particle swarm optimizer hybridized with extremal optimization
Applied Soft Computing
A review on particle swarm optimization algorithms and their applications to data clustering
Artificial Intelligence Review
A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
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
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Learning to play games using a PSO-based competitive learning approach
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Parameter selection and adaptation in Unified Particle Swarm Optimization
Mathematical and Computer Modelling: An International Journal
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Premature convergence has been recognized as one of the major drawbacks of particle swarm optimization PSO algorithms. In particular, the lack of diversity in PSO performance is an essential cause that commonly results in high susceptibility to prematurely converge to local optima especially in complex multimodal problems with high dimensionality. This paper presents a new PSO operational strategy based on gravity concept to address the aforementioned drawback and it is named as gravity-based particle swarm optimizer GPSO. In addition, GPSO is further modified by adopting the cooperation concept of the conventional cooperative particle swarm optimizer CPSO to develop an extended version of GPSO called cooperative gravity-based particle swarm optimizer CGPSO. Simulation results manifest that CGPSO performs satisfactorily on unimodal functions while it generally performs better on multimodal functions than GPSO and other conventional PSO variants. Finally, the proposed GPSO and CGPSO are applied into the problem of optimizing the detection performance of soft decision fusion for cooperative spectrum sensing in cognitive radio networks. For this problem, computer simulations show that the proposed CGPSO outperforms all other PSO variants in terms of quality of solutions whereas GPSO is found to be the best when the computational cost is taken into account.