How to solve it: modern heuristics
How to solve it: modern heuristics
Ant Colony Optimization
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Cooperative stalking of transient nomadic resources on overlay networks
Future Generation Computer Systems
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In many science fields such as physics, chemistry and engineering, the theory and experimentation complement and challenge each other. Algorithms are the most common form of problem solving in many science fields. All algorithms include parameters that need to be tuned with the objective of optimizing its processes. The NAS (Neighboring-Ant Search) algorithm was developed to route queries through the Internet. NAS is based on the ACS (Ant Colony System) metaheuristic and SemAnt algorithm, hybridized with local strategies such as: learning, characterization, and exploration. This work applies techniques of Design of Experiments for the analysis of NAS algorithm. The objective is to find out significant parameters for the algorithm performance and relations among them. Our results show that the probability distribution of the network topology has a huge significance in the performance of the NAS algorithm. Besides, the probability distributions of queries invocation and repositories localization have a combined influence in the performance.