Swarm intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
The hyperspherical acceleration effect particle swarm optimizer
Applied Soft Computing
A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing
Efficient population utilization strategy for particle swarm optimizer
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
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
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
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Particle swarm optimization PSO has been proposed as an alternative to traditional evolutionary algorithms. Yet, more efficient strategies are still needed to control the trade-off between exploitation and exploration in the search process for solving complex tasks with high dimensional and multimodal objective functions. In this work, the authors propose a new PSO approach to overcome the search difficulties. Their approach first predicts the landscape type of a function for initial search settings, and then focuses on two search strategies for multimodal functions. One is a two-swarm cooperative strategy that controls search region and integrates partial and full dimension PSO search. The other strategy is to control the velocity of the particles in an adaptive way, according to how they move in the space. To evaluate the proposed approach, extensive experiments have been conducted and comparisons to several popular PSO variants have been made. Our experiments prove that the proposed approach can have better performance than others in most of the test cases.