The Research On Ant Colony Algorithm For Solving The Service Selection Problem

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Ant colony optimization exploits a similar mechanism for solving optimization problems.We implemented a variety of ant colony optimization algorithms for solving the service selection problem. To tackle the large scale QoS-based service selection problem, we improved the basic ACO algorithms based on the concept of Skyline, clustering. In the enhanced ACO algorithms, a Skyline query process is used to filtering the candidates related each service class and a clustering based shrinking process is used to guide the ant search directions. To improve the performance of the enhanced ACO algorithms further, we combined the enhanced ACO algorithms with artificial bee colony (ABC).We also evaluated the performance of our improved ACO algorithms using standard real datasets and synthetically generated datasets on the issue of mono-objective service selection and compared with the recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solution. We also implemented a variety of ant colony optimization algorithms for solving multi-objective service selection problem and evaluated their performance through experiments and analysis.