Future Generation Computer Systems
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
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 system with communication strategies
Information Sciences—Informatics and Computer Science: An International Journal
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Neural Computing and Applications
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Multivariate ant colony optimization in continuous search spaces
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation
Connection Science - Evolutionary Learning and Optimisation
Hybrid evolutionary algorithms for large scale continuous problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Memetic algorithm with local search chaining for large scale continuous optimization problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Baldwinian learning in clonal selection algorithm for optimization
Information Sciences: an International Journal
Computational Optimization and Applications
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
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
ACO for continuous optimization based on discrete encoding
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
A study on scale factor in distributed differential evolution
Information Sciences: an International Journal
Differential evolution for parameterized procedural woody plant models reconstruction
Applied Soft Computing
Editorial: Special issue: Swarm intelligence and its applications
Information Sciences: an International Journal
Local measures of information storage in complex distributed computation
Information Sciences: an International Journal
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Determination of the heat transfer coefficient by using the ant colony optimization algorithm
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Environmental framework to visualize emergent artificial forest ecosystems
Information Sciences: an International Journal
Information Sciences: an International Journal
Multi-core implementation of the differential ant-stigmergy algorithm for numerical optimization
The Journal of Supercomputing
Parameter-less algorithm for evolutionary-based optimization
Computational Optimization and Applications
CAPSO: Centripetal accelerated particle swarm optimization
Information Sciences: an International Journal
The continuous differential ant-stigmergy algorithm for numerical optimization
Computational Optimization and Applications
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Ant-Colony Optimization (ACO) is a popular swarm intelligence scheme known for its efficiency in solving combinatorial optimization problems. However, despite some extensions of this approach to continuous optimization, high-dimensional problems remain a challenge for ACO. This paper presents an ACO-based algorithm for numerical optimization capable of solving high-dimensional real-parameter optimization problems. The algorithm, called the Differential Ant-Stigmergy Algorithm (DASA), transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned to the graph vertices are used to navigate through the search space. We compare the algorithm results with the results of previous studies on recent benchmark functions and show that the DASA is a competitive continuous optimization algorithm that solves high-dimensional problems effectively and efficiently.