An Adaptive Multiagent Strategy for Solving Combinatorial Dynamic Optimization Problems

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).