On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
A Population Based Approach for ACO
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th 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
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multivariate ant colony optimization in continuous search spaces
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Combined Ant Colony and Differential Evolution Feature Selection Algorithm
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A novel chemistry based metaheuristic optimization method for mining of classification rules
Expert Systems with Applications: An International Journal
An ant colony algorithm for solving the sky luminance model parameters
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Solving 0-1 knapsack problem by continuous ACO algorithm
International Journal of Computational Intelligence Studies
| Hi-index | 12.05 |
Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution. To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method. Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains.