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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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

Shihan Kong¹, Fang Wu², Hao Liu³

¹The State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China.

Biomimetics (Basel, Switzerland)

|July 26, 2024

Summary

This summary is machine-generated.

This study introduces a new algorithm (HPAGA) to optimize operations in radioactive environments by solving a complex radiation dose planning problem. The hyper-parameter adaptive genetic algorithm (HPAGA) efficiently finds optimal solutions for the variant traveling salesman problem (VTSP).

Keywords:

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

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Area of Science:

Operations Research
Artificial Intelligence
Radiation Safety

Background:

Radioactive environments pose complex operational planning challenges.
Accurate radiation dose modeling is crucial for safe and efficient operations.
Existing optimization methods may not adequately address multi-objective planning under radiation constraints.

Purpose of the Study:

To develop a novel approach for the multi-objective operating planning problem in radioactive environments.
To introduce a new combinatorial algorithm framework for efficient optimization.
To validate the proposed method's performance against established algorithms.

Main Methods:

Construction of a detailed radiation dose model incorporating operational difficulty.
Formulation of the planning problem as a variant traveling salesman problem (VTSP).
Development of a hyper-parameter adaptive genetic algorithm (HPAGA) integrating genetic algorithms (GA) and reinforcement learning (RL).

Main Results:

The proposed HPAGA demonstrates superior performance compared to classical evolutionary algorithms on various TSP instances.
HPAGA enables efficient, adaptive adjustment of genetic algorithm hyperparameters for optimal solution discovery.
Comparative studies confirm the effectiveness of the HPAGA framework.

Conclusions:

The HPAGA framework offers a powerful and efficient solution for multi-objective operating planning in radioactive settings.
The developed radiation dose model and VTSP formulation provide a robust basis for operational optimization.
The study highlights the potential of HPAGA for real-world applications in simulated radioactive environments.