Ant algorithms for discrete optimization
Artificial Life
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Search bias in ant colony optimization: on the role of competition-balanced systems
IEEE Transactions on Evolutionary Computation
Parametric study for an ant algorithm applied to water distribution system optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive approach for finding arbitrary roots of polynomials by neural networks
IEEE Transactions on Neural Networks
Zeroing polynomials using modified constrained neural network approach
IEEE Transactions on Neural Networks
Self-adaptive ant colony system for the traveling salesman problem
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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Presetting control parameters of algorithms are important to ant colony optimization (ACO). This paper presents an investigation into the relationship of algorithms performance and the different control parameter settings. Two tour building methods are used in this paper including the max probability selection and the roulette wheel selection. Four parameters are used, which are two control parameters of transition probability α andβ, pheromone decrease factor ρ, and proportion factor q0 in building methods. By simulated result analysis, the parameter selection rule will be given.