Ant colony optimization for continuous functions by using novel pheromone updating

摘要

This paper presents an ant colony optimization (ACO) algorithm for continuous functions based on novel pheromone updating. At the end of the each iteration in the proposed algorithm, pheromone is updated according to percentiles which determine the number of ants to track the best candidate solution. It is performed by means of solution archive and information provided by previous solutions. Performance of the proposed algorithm is tested on ten benchmark problems found in the literature and compared with performances of previous methods. The results show that ACO which is based on novel pheromone updating scheme (ACO-NPU) handles different types of continuous functions very well and can be a robust alternative approach to other stochastic search algorithms.