The cloud-based framework for ant colony optimization

Authors:
Zhiyong Li;Yong Wang;Kouassi K.S. Olivier;Jun Chen;Kenli Li
Affiliations:
School of Computer and Communication of Hunan University, Changsha, China;School of Computer and Communication of Hunan University, Changsha, China;School of Computer and Communication of Hunan University, Changsha, China;Office Of Student Admission of Hunan University, Changsha, China;School of Computer and Communication of Hunan University, Changsha, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

How to keep the balance between exploration in search space regions and exploitation of the search experience gathered so far is one of the most important issues in Ant Colony Optimization (ACO). By using a variety of effective exploitation mechanisms and elite strategies, researchers proposed many sophisticated ACO algorithms, and obtains better results in experiments. In this paper, a new framework for implementing ACO algorithms called the cloud-based framework for ACO is proposed, which uses cloud model as the fuzzy membership function and constructs a self-adaptive mechanism with cloud model. By using the self-adaptive mechanism and the pheromone updating rule of suboptimal solutions which is determined by the membership function uncertainly, the cloud-based framework can make ACO algorithm explorer search space more effectively. Theoretical analysis on the cloud-based framework for ACO indicate that the framework is convergent, and the simulation results show that the framework can improve the ACO algorithms evidently.