New ideas in optimization
On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
A Method for Solving Optimization Problems in Continuous Space Using Ant Colony Algorithm
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
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
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
An incremental ant colony algorithm with local search for continuous optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An ACO algorithm benchmarked on the BBOB noiseless function testbed
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
The Ant Colony Optimization (ACO) metaheuristic embodies a large set of algorithms which have been successfully applied to a wide range of optimization problems. Although ACO practitioners have a long tradition in solving combinatorial optimization problems, many other researchers have recently developed a variety of ACO algorithms for dealing with continuous optimization problems. One of these algorithms is the so-called ACOR, which is one of the most relevant ACO algorithms currently available for continuous optimization problems. Although ACOR has been found to be successful, to the authors' best knowledge its use in high-dimensionality problems (i.e., with many decision variables) has not been documented yet. Such problems are important, because they tend to appear in real-world applications and because in them, diversity loss becomes a critical issue. In this paper, we propose an alternative ACOR algorithm (DACOR) which could be more appropriate for large scale unconstrained continuous optimization problems. We report the results of an experimental study by considering a recently proposed test suite. In addition, the parameters setting of the algorithms involved in the experimental study are tuned using an ad hoc tool. Our results indicate that our proposed DACOR is able to improve both, the quality of the results and the computational time required to achieve them.