Future Generation Computer Systems
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
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
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimizing the shape of an impeller using the differential ant-stigmergy algorithm
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
cAS: ant colony optimization with cunning ants
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
DACCO: a discrete ant colony algorithm to cluster geometry optimization
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In previous papers we proposed an algorithm for real parameter optimization called the Aggregation Pheromone System (APS). The APS replaces pheromone trails in traditional ACO with aggregation pheromones. The pheromone update rule is applied in a way similar to that of ACO. In this paper, we proposed an enhanced APS (eAPS), which uses a colony model with units. It allows a stronger exploitation of better solutions found and at the same time it can prevent premature stagnation of the search. Experimental results showed eAPS has higher performance than APS. It has also shown that the parameter settings for eAPS are more robust than for APS.