The cloud-based framework for ant colony optimization Export

@inproceedings{citeulike:5291340, 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.}, address = {New York, NY, USA}, author = {Li, Zhiyong and Wang, Yong and Olivier, Kouassi K. S. and Chen, Jun and Li, Kenli}, booktitle = {GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation}, citeulike-article-id = {5291340}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1543872}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1543834.1543872}, doi = {10.1145/1543834.1543872}, isbn = {978-1-60558-326-6}, keywords = {2009, ant-system, framework, metaheuristics, optimization}, location = {Shanghai, China}, pages = {279--286}, posted-at = {2009-07-28 15:16:27}, priority = {2}, publisher = {ACM}, title = {The cloud-based framework for ant colony optimization}, url = {http://dx.doi.org/10.1145/1543834.1543872}, year = {2009} }

TY - CONF ID - citeulike:5291340 L3 - citeulike-article-id:5291340 N2 - 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. CY - Shanghai, China SN - 978-1-60558-326-6 AD - New York, NY, USA EP - 286 TI - The cloud-based framework for ant colony optimization PB - ACM T2 - GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation SP - 279 KW - 2009 KW - ant-system KW - framework KW - metaheuristics KW - optimization AU - Li, Zhiyong AU - Wang, Yong AU - Olivier, Kouassi AU - Chen, Jun AU - Li, Kenli PY - 2009/// UR - http://dx.doi.org/10.1145/1543834.1543872 ER -