An Agent Based Implementation of Proactive S-Metaheuristics
This paper presents the use of a multi-agent system for the development of proactive S-Metaheuristics (i.e. single-solution based metaheuristics) derived from Record-to-Record Travel (RRT) and Local Search. The basic idea is to implement metaheuristics as agents that operate in the environment of the optimization process with the goal of avoiding stagnation in local optima by adjusting their parameters and neighborhood. Environmental information about previous solutions is used to determine the best operators and parameters. The proactive adjustment of the neighborhood is based on the identification of the best operators using Fitness Distance Correlation (FDC). The proactive adjustment of the parameters is focused on guarantying a minimal level of acceptance of new solutions. Besides, a simple form of combination of both proactive behaviors is introduced. The system has been validated through experimentation with 28 functions on binary strings.
KeywordsMetaheuristics Agents Proactivity Local Search RRT FDC
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