Chaos Ant Colony Optimization and Application

Abstract

Swarm intelligence exhibits a number of interesting properties such as flexibility, robustness, decentralization and self-organization. The instances of these algorithms on the domains of optimization, telecommunication network, knowledge discovery and robots are obviously increased. An ant colony algorithm is proposed aiming at the basic ant colony algorithms convergence slow and be prone to plunge a partial basis. By use of the properties of ergodicity, randomicity, and regularity of chaos, a chaos ant colony optimization algorithm is proposed. Chaos system is introduced into the ant colony algorithm, and pheromone updating strategy is ameliorated to improve the efficiency and control the shortest route and evolution times. Results show that chaos ant colony optimization is a simple and effective algorithm in decision.