A Two-Level Function Evaluation Management Model For Multi-Population Methods In Dynamic Environments: Hierarchical Learning Automata Approach

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE(2021)

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摘要
The fitness evaluation (FE) management has been successfully applied to improve the performance of multi-population methods for dynamic optimisation problems (DOPs). In this work, we extend one of its variants to address DOPs which was recently proposed by the authors. The aim of our proposal is to increase the efficiency of the FE management. To this end, we propose a technique based on hierarchical learning automata that manages FEs at two level: at first level the algorithm decides which population should be executed, and at the second level it specifies the operation that should be performed by the selected population. A detailed experimental analysis shows the effectiveness of our proposal.
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关键词
Dynamic optimisation problems, differential evolution, moving peaks benchmark, evolutionary computation, hierarchical learning automata, function evaluation management
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