Abstract
Among the existing techniques to improve the performance of metaheuristics in optimization problems, adaptive parameter control consists in varying one or more parameters of a given metaheuristic according to some indicator of the search conditions. This approach allows metaheuristics to change algorithmic behaviour during the search, and is particularly relevant for the optimization of dynamic problems. In this research we theoretically analyse in which ways the parameters of the ant colony optimization for continuous domains metaheuristic can be adapted, regarding how each parameter influences exploration and exploitation characteristics of the algorithm. Our experimental contributions include validating the colony success rate as a search condition estimator and choosing suitable maps from this estimator to the parameters q and \(\xi \) of the algorithm. Beyond that, we compare the performances of three proposed adaptive versions of the base metaheuristic and show the benefits of simultaneously adapting multiple parameters.
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Costa, V.O., Müller, F.M. (2020). On the Multiple Possible Adaptive Mechanisms of the Continuous Ant Colony Optimization. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_12
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