Bio-Inspired Optimization Algorithm Associated with Reinforcement Learning for Multi-Objective Operating Planning in Radioactive Environment

Shihan Kong, Fang Wu, Hao Liu, Wei Zhang,Jinan Sun,Jian Wang,Junzhi Yu

Biomimetics（2024）

The State Key Laboratory for Turbulence and Complex Systems | SPIC Nucl Energy Co Ltd | Beijing Univ Chem Technol | Peking Univ | The Laboratory of Cognitive and Decision Intelligence for Complex System

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Abstract

This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future.

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Key words

reinforcement learning,improved genetic algorithm,radioactive environment planning,bio-inspired optimization algorithm,combinatorial algorithm

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