Abstract
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).
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González, J.R., Cruz, C., del Amo, I.G., Pelta, D.A. (2011). An Adaptive Multiagent Strategy for Solving Combinatorial Dynamic Optimization Problems. In: Pelta, D.A., Krasnogor, N., Dumitrescu, D., Chira, C., Lung, R. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2011). Studies in Computational Intelligence, vol 387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24094-2_3
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DOI: https://doi.org/10.1007/978-3-642-24094-2_3
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