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

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 convert...

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Bibliographic Details
Published in:Biomimetics (Basel, Switzerland) Vol. 9; no. 7; p. 438
Main Authors: Kong, Shihan, Wu, Fang, Liu, Hao, Zhang, Wei, Sun, Jinan, Wang, Jian, Yu, Junzhi
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 17.07.2024
Subjects:
Algorithms
bio-inspired optimization algorithm
combinatorial algorithm
Genetic algorithms
Heuristic
improved genetic algorithm
Integer programming
Mutation
Optimization algorithms
Optimization techniques
Planning
Radiation
radioactive environment planning
Reinforcement
reinforcement learning
Robots
Traveling salesman problem
combinatorial algorithm
radioactive environment planning
improved genetic algorithm
bio-inspired optimization algorithm
reinforcement learning
ISSN:2313-7673, 2313-7673
Online Access:Get full text
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Summary: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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ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics9070438