An Alternative ACO$_{\Bbb{R}}$ Algorithm for Continuous Optimization Problems

Abstract

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 ACO$_{\Bbb{R}}$, which is one of the most relevant ACO algorithms currently available for continuous optimization problems. Although ACO$_{\Bbb{R}}$ 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 ACO$_{\Bbb{R}}$ algorithm (DACO$_{\Bbb{R}}$) 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 DACO$_{\Bbb{R}}$ is able to improve both, the quality of the results and the computational time required to achieve them.