Continuous ant colony optimization algorithm based on crossover and mutation

摘要

In this article, the ant colony optimization (ACO, in short) algorithm for solving continuous space optimization problems are discussed. Both of the way of the pheromone remains and the searching strategy is defined. At the same time, this algorithm which is easily trapped into local optimum is improved by carrying on fine searching near the best ant and adding the crossover and mutation operator, so that the global convergence performance of ACO is enhanced. The numerical simulation results demonstrate that the proposed algorithm is effective.