Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Swarm intelligence: from natural to artificial systems
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Future Generation Computer Systems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Ant Colony Optimization
Continuous interacting ant colony algorithm based on dense heterarchy
Future Generation Computer Systems - Special issue: Computational chemistry and molecular dynamics
A binary ant colony optimization for the unconstrained function optimization problem
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
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ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The differential ant-stigmergy algorithm
Information Sciences: an International Journal
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This paper proposes a direct application of Ant Colony Optimization to the function optimization problem in continuous domain. In the proposed algorithm, artificial ants construct solutions by selecting values for each variable randomly biased by a specific variable-related normal distribution, of which the mean and deviation values are represented by pheromone modified by ants according to the previous search experience. Some methods to avoid premature convergence, such as local search in different neighborhood structure, pheromone re-initialization and different solutions for pheromone intensification are incorporated into the proposed algorithm. Experimental setting of the parameters are presented, and the experimental results show the potential of the proposed algorithm in dealing with the function optimization problem of different characteristics.