{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T14:51:43Z","timestamp":1780757503167,"version":"3.54.1"},"reference-count":18,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm.<\/jats:p>","DOI":"10.3233\/jifs-211214","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:10:16Z","timestamp":1638886216000},"page":"1813-1823","source":"Crossref","is-referenced-by-count":59,"title":["A novel evacuation path planning method based on improved genetic algorithm"],"prefix":"10.1177","volume":"42","author":[{"given":"Longzhen","family":"Zhai","sequence":"first","affiliation":[{"name":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaohong","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211214_ref1","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ssci.2018.03.015","article-title":"Exit choice in an emergency evacuation scenario is influenced by exit familiarity and neighbor behavior","volume":"106","author":"Kinateder","year":"2018","journal-title":"Safety Science"},{"key":"10.3233\/JIFS-211214_ref2","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.ssci.2018.11.028","article-title":"Modelling building emergency evacuation plans considering the dynamic behaviour of pedestrians using agent-based simulation","volume":"113","author":"Rozo","year":"2019","journal-title":"Safety Science"},{"issue":"3","key":"10.3233\/JIFS-211214_ref3","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1016\/j.ejor.2014.02.054","article-title":"Modeling framework for optimal evacuation of large-scale crowded pedestrian facilities","volume":"237","author":"Abdelghany","year":"2014","journal-title":"European Journal of Operational Research"},{"issue":"6","key":"10.3233\/JIFS-211214_ref4","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1631\/jzus.2005.A0549","article-title":"Neural network and genetic algorithm based global path planning in a static environment","volume":"6","author":"Xin","year":"2005","journal-title":"Journal of Zhejiang University-Science A"},{"issue":"11","key":"10.3233\/JIFS-211214_ref5","first-page":"110","article-title":"Application research of artificial neural network in robot trajectory planning","volume":"21","author":"Jun","year":"2005","journal-title":"Chinese. 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