An Empirical Comparison of Particle Swarm and Predator Prey Optimisation
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A new approach to improve particle swarm optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Novel KNN-motivation-PSO and its application to image segmentation
Proceedings of the CUBE International Information Technology Conference
The Multilayer Random Neural Network
Neural Processing Letters
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Adaptive inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for particle swarm optimization. The resulting algorithm is called adaptive inertia weight particle swarm optimization algorithm (AIW-PSO) where a simple and effective measure, individual search ability (ISA), is defined to indicate whether each particle lacks global exploration or local exploitation abilities in each dimension. A transform function is employed to dynamically calculate the values of inertia weight according to ISA. In each iteration during the run, every particle can choose appropriate inertia weight along every dimension of search space according to its own situation. By this fine strategy of dynamically adjusting inertia weight, the performance of PSO algorithm could be improved. In order to demonstrate the effectiveness of AIW-PSO, comprehensive experiments were conducted on three well-known benchmark functions with 10, 20, and 30 dimensions. AIW-PSO was compared with linearly decreasing inertia weight PSO, fuzzy adaptive inertia weight PSO and random number inertia weight PSO. Experimental results show that AIW-PSO achieves good performance and outperforms other algorithms.