An Adaptive Chaotic Ant Colony Optimization Algorithm Based on Logistic Mapping

Ant colony optimization algorithm (ACO) is a heuristic bionic evolutionary system based on the population. The positive feedback of ACO helps to search the approximate optimal solution, however, it also makes it easier to get trapped in local optimal solution. In order to avoid prematurity and enhance its adaptability, this paper introduces chaos principle in ACO and proposes an adaptive chaotic ant colony optimization algorithm (ACACO) based on Logistic mapping. ACACO integrates ACO with chaos theory, adds chaotic disturbance variable into the pheromone update and performs local search after global search, in this way, the algorithm will moves once again around the optimal solution and increases the probability to search the global optimal solution. The experiment simulation and analysis to be made to the algorithm of this paper through four benchmark functions show that ACACO is better at solving high-dimensional optimization problems.