An Improved Ant Colony Optimization for QoS-Aware Web Service Composition

Web service composition (WSC) provides a flexible framework for integrating independent Web services to meet complex functional needs. The Web service selection (WSS) problem centers on selecting the best service from a set of candidate Web services based on quality of service (QoS) features. In this paper, we propose an adaptive chaotic ant colony optimization algorithm for multi-pheromone distribution based on the swap concept. The aim of the improvement to the ACO is to avoid local optimum traps and reduce the search time. Chaotic disturbances and integration of multiple solutions will increase the chances of the algorithm getting an optimal solution and avoid stagnation, while multiple pheromones of QoS are used to enhance the exploration of the solution space. Experimental analysis of the algorithm with ACO and FACO shows that it outperforms the latter two ant colony optimization algorithms in terms of quality of solution, standard deviation and execution time.