Chaotic Inertia Weight in Particle Swarm Optimization
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
A Method of Self-Adaptive Inertia Weight for PSO
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
ICIC '09 Proceedings of the 2009 Second International Conference on Information and Computing Science - Volume 01
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
DNA Sequence Compression Using Adaptive Particle Swarm Optimization-Based Memetic Algorithm
IEEE Transactions on Evolutionary Computation
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The exponential inertia weight is proposed in this work aiming to improve the search quality of Particle Swarm Optimization (PSO) algorithm. This idea is based on the adaptive crossover rate used in Differential Evolution (DE) algorithm. The same formula is adopted and applied to inertia weight, w. We further investigate the characteristics of the adaptive w graphically and careful analysis showed that there exists two important parameters in the equation for adaptive w; one acting as the local attractor and the other as the global attractor. The 23 benchmark problems are adopted as test bed in this study; consisting of both high and low dimensional problems. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is used widely in literature.