Evolving ant colony system for optimizing path planning in mobile robots
CERMA '07 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference
An Adaptive Parameter Control Strategy for Ant Colony Optimization
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Performance Evaluation of an Adaptive Ant Colony Optimization Applied to Single Machine Scheduling
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
MC-ANT: a multi-colony ant algorithm
EA'09 Proceedings of the 9th international conference on Artificial evolution
IEEE Computational Intelligence Magazine
A SURVEY OF QOS ROUTING SOLUTIONS FOR MOBILE AD HOC NETWORKS
IEEE Communications Surveys & Tutorials
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Parameter tuning of metaheuristic is the process of finding and controlling correct combination and values of an algorithm's parameters for each individual problem. Since the performance of Ant Colony Optimization ACO is influenced by its parameter values, many techniques were proposed in the literature to tune the parameters in ACO. This is because parameters can implicitly determine the amplification and diversification of the search process. ACO is applied to a variety of optimization problems and, unfortunately, there are no universal parameter values which can be used in ACO to solve all kinds of real-world optimization problems efficiently and effectively due to the differences in size and type of these real-world applications. In this paper, we present a mechanism using Particle Swarm Optimization PSO to adaptively tune the parameters of ACO using different ranges for each parameter. The parameter-tuned ACO is applied to provide Quality of Service routing in mobile ad-hoc network MANET. The performance of the parameter-tuned ACO is compared with a non-adaptive ACO version.