{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T12:54:30Z","timestamp":1777380870312,"version":"3.51.4"},"reference-count":40,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIS"],"published-print":{"date-parts":[[2022,3,24]]},"abstract":"<jats:p>Cities are in a period of rapid urban development and high-rise buildings are constantly emerging. The characteristics of a fire in a high-rise building are the rapid spread of the fire, the difficulty of fighting the fire, and the difficulty of evacuation. Intelligent fire evacuation requires dynamic planning of paths in fire field, it is necessary to automatically adjust the evacuation route in the building according to the real-time information of the fire. In this paper, an improved bidirectional ant colony algorithm is proposed to optimize fire evacuation routes. In order to improve the global search capability of the algorithm, a bidirectional search strategy with the A* algorithm is designed for the ant colony algorithm, the blindness of the algorithm is reduced in the initial search, the pheromone update strategy is improved, and the convergence speed of the algorithm is increased. The fire scene information is combined with the steering penalty coefficient to improve the algorithm\u2019s evaporation coefficient, heuristic function and transition probability, avoid the risk of falling into the local optimum, improve the search efficiency of the algorithm and the smoothness of the path, and effectively avoid areas affected by the fire. The effectiveness of the algorithm is verified by simulation.<\/jats:p>","DOI":"10.3233\/ais-220620","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T12:02:33Z","timestamp":1647345753000},"page":"79-97","source":"Crossref","is-referenced-by-count":4,"title":["Bidirectional ACO intelligent fire evacuation route optimization"],"prefix":"10.1177","volume":"14","author":[{"given":"Jingfang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AIS-220620_ref1","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.ssci.2018.10.027","article-title":"Performance modeling of an intelligent emergency evacuation system in buildings on accidental fire occurrence","volume":"112","author":"Angel","year":"2019","journal-title":"Safety science"},{"issue":"4","key":"10.3233\/AIS-220620_ref2","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.11772\/j.issn.1001-9081.2018091977","article-title":"Application of improved A \u2217 algorithm in indoor path planning for mobile robot","volume":"39","author":"Chen","year":"2019","journal-title":"Journal of Computer Applications"},{"key":"10.3233\/AIS-220620_ref3","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.simpat.2019.04.002","article-title":"Optimal route selection model for fire evacuations based on hazard prediction data","volume":"94","author":"Choi","year":"2019","journal-title":"Simulation Modelling Practice and Theory"},{"key":"10.3233\/AIS-220620_ref4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.autcon.2018.11.020","article-title":"Optimal path plan ning in real time for dynamic building fire rescue opera tions using wireless sensors and visual guidance","volume":"99","author":"Chou","year":"2019","journal-title":"Automation in Construction"},{"key":"10.3233\/AIS-220620_ref5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.autcon.2018.11.020","article-title":"Optimal path planning in real time for dynamic building fire rescue operations using wireless sensors and visual guidance","volume":"99","author":"Chou","year":"2019","journal-title":"Automation in construction"},{"issue":"1","key":"10.3233\/AIS-220620_ref6","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/4235.585892","article-title":"Ant colony system: A cooperative learning approach to the traveling salesman problem","volume":"1","author":"Dorigo","year":"1997","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"5","key":"10.3233\/AIS-220620_ref7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.11835\/j.issn.1000-582X.2020.05.004","article-title":"Design of laser intelligent fire-fighting evacuation system based on ant colony algorithm","volume":"43","author":"Feng","year":"2020","journal-title":"Journal of Chongqing University(Natural Science Edition)"},{"key":"10.3233\/AIS-220620_ref8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.autcon.2020.103353","article-title":"BIM-based automated determination of exit sign direction for intelligent building sign systems","volume":"120","author":"Fu","year":"2020","journal-title":"Automation in construction"},{"issue":"2","key":"10.3233\/AIS-220620_ref9","doi-asserted-by":"publisher","first-page":"143","DOI":"10.13614\/j.cnki.11-1962\/tu.2016.02.24","article-title":"Evacuation path guidance based on BIM","volume":"32","author":"Gao","year":"2016","journal-title":"Building Science"},{"key":"10.3233\/AIS-220620_ref10","first-page":"28","article-title":"A hybrid of ant colony and firefly algorithms (HAFA) for solving vehicle routing problems","volume":"25","author":"Goel","year":"2018","journal-title":"Journal of Safety and Environment"},{"issue":"7","key":"10.3233\/AIS-220620_ref11","doi-asserted-by":"publisher","first-page":"702","DOI":"10.11936\/bjutxb2020120035","article-title":"Dynamic evacuation path planning for fire disaster of deep underground space based on A* algorithm","volume":"47","author":"Huang","year":"2021","journal-title":"Journal of Beijing University of Technology"},{"issue":"1","key":"10.3233\/AIS-220620_ref12","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1177\/1420326X19842370","article-title":"Computational fluid dynamics-assisted smoke control system design for solving fire uncertainty in buildings. 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