Phelogenetic Tree Construction using Self adaptive Ant Colony Algorithm
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Ant Colony Optimization Algorithm Based on Adaptive Weight and Volatility Parameters
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 02
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An intelligent testing system embedded with an ant-colony-optimization-based test composition method
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Continuous function optimization using hybrid ant colony approach with orthogonal design scheme
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Ant colony optimization (ACO) has been widely and successfully applied to NP-hard combinatorial optimization problems for its strong searching ability and robustness. Recently, several extended ACO algorithms have also been proposed to deal with continuous optimization problems. However, the ACO algorithms always have slow convergence speed and encounter premature convergence in engineering applications. This paper proposes a novel adaptive parameter control method for continuous ACO algorithms. Clustering analysis is used to judge the optimization state of the algorithm and the flexible adjustment of the parameters is based on these optimization states during the training process. As an example, the adaptive control method is used to improve the performance of the continuous orthogonal ant colony (COAC). Experimental results demonstrate that the clustering-based adaptive parameters control scheme contributes to both faster convergence speed and higher solution accuracy. The proposed adaptive control method has great practical value and bright prospect.