Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Multi-population cooperative particle swarm optimization
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
A multi-population cooperative particle swarm optimizer for neural network training
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A Swarm-Based Learning Method Inspired by Social Insects
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
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By introducing the information entropy H(X) and mutual information I(X;Y) of information theory into swarm intelligence, the Interaction Optimization Model (IOM) is proposed. In this model, the information interaction process of individuals is analyzed with H(X) and I(X;Y) aiming at solving optimization problems. We call this optimization approach as interaction optimization. In order to validate this model, we proposed a new algorithm for Traveling Salesman Problem (TSP), namely Route-Exchange Algorithm (REA), which is inspired by the information interaction of individuals in swarm intelligence. Some benchmarks are tested in the experiments. The results indicate that the algorithm can quickly converge to the optimal solution with quite low cost.