This work presents the results obtained when using a decentralised multiagent strategy (Agents) to solve dynamic optimization problems of a combinatorial nature. To improve the results of the strategy, we also include a simple adaptive scheme for several configuration variants of a mutation operator in order to obtain a more robust behaviour. The adaptive scheme is also tested on an evolutionary algorithm (EA). Finally, both Agents and EA are compared against the recent state of the art adaptive hill-climbing memetic algorithm (AHMA). © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
González, J. R., Cruz, C., Del Amo, I. G., & Pelta, D. A. (2011). An adaptive multiagent strategy for solving combinatorial dynamic optimization problems. In Studies in Computational Intelligence (Vol. 387, pp. 41–55). https://doi.org/10.1007/978-3-642-24094-2_3
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