https://scholars.lib.ntu.edu.tw/handle/123456789/598999
Title: | Metaheuristic minimum dose path planning for nuclear power plant decommissioning | Authors: | Lai Y.-C Smith S. SHANA SMITH |
Keywords: | Genetic algorithm;Metaheuristic;Minimum dose path planning;Particle swarm optimization;Biomimetics;Decommissioning (nuclear reactors);Genetic algorithms;Motion planning;Nuclear energy;Nuclear fuels;Nuclear power plants;Decommissioning strategies;Grid-based;Meta-heuristic methods;Operational life;Optimal paths;Power plant decommissioning;Radioactive environment;Workers';Particle swarm optimization (PSO) | Issue Date: | 2022 | Journal Volume: | 166 | Source: | Annals of Nuclear Energy | Abstract: | When a nuclear power plant reaches its end of operational life, decommissioning work needs to be carried out. One of the most important decommissioning strategies is to plan an optimal path for workers or robots to move around in a radioactive environment, keeping the radiation exposure as low as possible. This paper develops two bio-inspired metaheuristic methods for minimum dose path planning using a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), respectively. To evaluate the effectiveness of the two metaheuristic methods, two extreme hypothetical environments are simulated. The developed bio-inspired metaheuristic methods are compared with prior grid-based and sampling-based minimum dose path planning algorithms, in terms of cumulative dose, computational time, and distance. The results indicate that PSO outperforms prior grid-based and sampling-based algorithms in cumulative dose and distance. GA outperforms only the grid-based algorithms in cumulative dose and distance. ? 2021 Elsevier Ltd |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119266674&doi=10.1016%2fj.anucene.2021.108800&partnerID=40&md5=61d9dea97897d2ff3926fc8de647ca92 https://scholars.lib.ntu.edu.tw/handle/123456789/598999 |
ISSN: | 03064549 | DOI: | 10.1016/j.anucene.2021.108800 |
Appears in Collections: | 機械工程學系 |
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