Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
On how pachycondyla apicalis ants suggest a new search algorithm
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
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Ant Colony Optimization
Continuous interacting ant colony algorithm based on dense heterarchy
Future Generation Computer Systems - Special issue: Computational chemistry and molecular dynamics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Stigmergy-Based Algorithm for Continuous Optimization Tested on Real-Life-Like Environment
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
A modified binary differential evolution algorithm
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
The differential ant-stigmergy algorithm
Information Sciences: an International Journal
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This paper proposes a Binary Ant System (BAS), a binary version of the hyper-cube frame for Ant Colony Optimization applied to unconstrained function optimization problem. In BAS, artificial ants construct the solutions by selecting either 0 or 1 at every bit stochastically biased by the pheromone level. For ease of implementation, the pheromone value is designed specially to directly represent the probability of selection. Principal settings of the parameters are analyzed and some methods to escape local optima, such as local search and pheromone re-initialization are incorporated into the proposed algorithm. Experimental results show that the BAS is able to find very good results for the unconstrained function optimization problems of different characteristics.