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Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimisation
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DOI：10.1016/j.knosys.2019.03.003.png)
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Abstract
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There is an ongoing evolution involving a new approach to large-scale optimisations based on coevolutionary searches using interacting heterogeneous agent-processes via the implementation of synchronised genetic algorithms with local populations. The individualisation of heuristic operators at the level of agent-processes that implement independent evolutionary searches facilitate the improved likelihood of obtaining the best solutions in the fastest time. Based on this property, a parallel multi-agent single-objective real-coded genetic algorithm for large-scale constrained black-box single-objective optimisations (LSOPs) is proposed. This facilitates the effective frequency exchange of the best potential decisions between interacting agent-processes with individual parameters, such as types of crossover and mutation operators with their own characteristics. We have improved the quality of both solutions and the time-efficiency of a multi-agent real-coded genetic algorithm (MA - RCGA). A novel framework was developed that represents the aggregation of MA - RCGA with simulation models by implementing a set of objective functions for real-world large-scale optimisation problems such as the simulation model of the ecological-economics system implemented in the AnyLogic tool. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Real-coded genetic algorithms
Multi-agent genetic algorithms
Black-box single-objective optimisation
Simulation modelling
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