Lai Y.-CSmith S.SHANA SMITH2022-03-222022-03-22202203064549https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119266674&doi=10.1016%2fj.anucene.2021.108800&partnerID=40&md5=61d9dea97897d2ff3926fc8de647ca92https://scholars.lib.ntu.edu.tw/handle/123456789/598999When 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 LtdGenetic algorithmMetaheuristicMinimum dose path planningParticle swarm optimizationBiomimeticsDecommissioning (nuclear reactors)Genetic algorithmsMotion planningNuclear energyNuclear fuelsNuclear power plantsDecommissioning strategiesGrid-basedMeta-heuristic methodsOperational lifeOptimal pathsPower plant decommissioningRadioactive environmentWorkers'Particle swarm optimization (PSO)journal article10.1016/j.anucene.2021.1088002-s2.0-85119266674