{"title":"The multi-objective inspection path-planning in radioactive environment based on an improved ant colony optimization algorithm","authors":"Xingwen Xie, Zhihong Tang, Jiejin Cai","doi":"10.1016/j.pnucene.2021.104076","DOIUrl":null,"url":null,"abstract":"<div><p>More and more occupational workers work in radioactive environment. Although measures are taken to keep the radiation dose in a safe range, the workers will suffer more radiation during the overhauling of nuclear power plants<span>. The dose they suffer during the overhauling of nuclear power plants account for 80% of the total annual dose so it is necessarily to plan a reasonable inspection path for them according to the safety principle of as low as reasonably achievable (ALARA). An improved ant colony optimization<span> (IACO) algorithm is proposed to solve the multi-objective inspection path-planning problem in radioactive environment. To improve the performance of the algorithm, we not only combine ant colony optimization (ACO) algorithm and chaos optimization algorithm, but also introduce pheromone differentiated update strategy and local search optimization strategy<span>. Additionally, 5 experimental simulation cases are conducted and the results are compared to those from particle swarm optimization<span> (PSO) algorithm, chaos particle swarm optimization (CPSO) algorithm and tradition ACO algorithm. In the first three cases, the probability of IACO algorithm finding the optimal path is obviously greater than that of PSO algorithm and CPSO algorithm. IACO algorithm is more efficient and stable. By analyzing an inspection path-planning case with 35 target positions and a case with 44 target positions in a more complex radioactive environment, IACO algorithm could find the path with less effective dose that ACO algorithm cannot find. Therefore, the effectiveness and validity of IACO to solve multi-objective inspection path-planning problem in radioactive environment are verified by experimental simulations and it can help workers reduce radiation exposure.</span></span></span></span></p></div>","PeriodicalId":20617,"journal":{"name":"Progress in Nuclear Energy","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Nuclear Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0149197021004303","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 13
Abstract
More and more occupational workers work in radioactive environment. Although measures are taken to keep the radiation dose in a safe range, the workers will suffer more radiation during the overhauling of nuclear power plants. The dose they suffer during the overhauling of nuclear power plants account for 80% of the total annual dose so it is necessarily to plan a reasonable inspection path for them according to the safety principle of as low as reasonably achievable (ALARA). An improved ant colony optimization (IACO) algorithm is proposed to solve the multi-objective inspection path-planning problem in radioactive environment. To improve the performance of the algorithm, we not only combine ant colony optimization (ACO) algorithm and chaos optimization algorithm, but also introduce pheromone differentiated update strategy and local search optimization strategy. Additionally, 5 experimental simulation cases are conducted and the results are compared to those from particle swarm optimization (PSO) algorithm, chaos particle swarm optimization (CPSO) algorithm and tradition ACO algorithm. In the first three cases, the probability of IACO algorithm finding the optimal path is obviously greater than that of PSO algorithm and CPSO algorithm. IACO algorithm is more efficient and stable. By analyzing an inspection path-planning case with 35 target positions and a case with 44 target positions in a more complex radioactive environment, IACO algorithm could find the path with less effective dose that ACO algorithm cannot find. Therefore, the effectiveness and validity of IACO to solve multi-objective inspection path-planning problem in radioactive environment are verified by experimental simulations and it can help workers reduce radiation exposure.
期刊介绍：
Progress in Nuclear Energy is an international review journal covering all aspects of nuclear science and engineering. In keeping with the maturity of nuclear power, articles on safety, siting and environmental problems are encouraged, as are those associated with economics and fuel management. However, basic physics and engineering will remain an important aspect of the editorial policy. Articles published are either of a review nature or present new material in more depth. They are aimed at researchers and technically-oriented managers working in the nuclear energy field.
Please note the following:
1) PNE seeks high quality research papers which are medium to long in length. Short research papers should be submitted to the journal Annals in Nuclear Energy.
2) PNE reserves the right to reject papers which are based solely on routine application of computer codes used to produce reactor designs or explain existing reactor phenomena. Such papers, although worthy, are best left as laboratory reports whereas Progress in Nuclear Energy seeks papers of originality, which are archival in nature, in the fields of mathematical and experimental nuclear technology, including fission, fusion (blanket physics, radiation damage), safety, materials aspects, economics, etc.
3) Review papers, which may occasionally be invited, are particularly sought by the journal in these fields.
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