Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection

Continuous ant colony optimization was a population-based heuristic search algorithm inspired by the pathfinding behavior of ant colonies with a simple structure and few control parameters. However, in the case of multimodal and high-dimensional optimization problems, it was often limited to local regions in the feasible domain space, negatively affecting the computational effort required to find the optimal solution point. To alleviate its limitations in this regard, a random following strategy is proposed to enhance communication among the ant colony search agent and other ant colony members within the search dimension. The proposed algorithm that incorporates this strategy is called Random Following Ant Colony Optimization. Then, to evaluate the global optimization performance of the proposed algorithm, the well-known numerical optimization problem, namely the Congress on Evolutionary Computation 2017 test suite, is used. First, the proposed algorithm’s parameters are analyzed for sensitivity, scalability experiments, and balanced diversity. Second, it is compared experimentally with 11 state-of-the-art algorithms in dimensions 10, 30, 50, and 100, respectively, and Wilcoxon signed-rank test, Friedman test, and Bonferroni-Dunn post-hoc statistical test are used to synthesize the experimental comparison results. Finally, to evaluate the ability of the proposed algorithm to handle discrete feature selection problems, comparative experiments are conducted on 24 datasets with eight well-known classification methods and five high-performance classification methods. The benchmark test results show that the global optimization performance of the proposed algorithm is comparable to the winners of the test suite in 50 and 100 dimensions. The results of the feature selection experiments show that the proposed algorithm is much stronger than the well-known and high-performance classification methods on high-dimensional datasets.