Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System

Hindawi Journal of Applied Mathematics Volume 2018, Article ID 7962952, 17 pages https://doi.org/10.1155/2018/7962952 Research Article Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System Chen Wang ,1 Lincoln C. Wood,2 Heng Li,3 Zhenye Aw,4 and Abolfazl Keshavarzsaleh4 1 College of Civil Engineering, Huaqiao University, Xiamen 361021, China Department of Management, University of Otago, Dunedin 9054, New Zealand 3 Department of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 4 Faculty of Built Environment, University of Malaya, 50603 Kuala Lumpur, Malaysia 2 Correspondence should be addressed to Chen Wang; Received 8 January 2018; Accepted 20 March 2018; Published 24 April 2018 Academic Editor: Guan H. Yeoh Copyright © 2018 Chen Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Every minute counts in an event of fire evacuation where evacuees need to make immediate routing decisions in a condition of low visibility, low environmental familiarity, and high anxiety. However, the existing fire evacuation routing models using various algorithm such as ant colony optimization or particle swarm optimization can neither properly interpret the delay caused by congestion during evacuation nor determine the best layout of emergency exit guidance signs; thus bee colony optimization is expected to solve the problem. This study aims to develop a fire evacuation routing model “Bee-Fire” using artificial bee colony optimization (BCO) and to test the routing model through a simulation run. Bee-Fire is able to find the optimal fire evacuation routing solutions; thus not only the clearance time but also the total evacuation time can be reduced. Simulation shows that BeeFire could save 10.12% clearance time and 15.41% total evacuation time; thus the congestion during the evacuation process could be effectively avoided and thus the evacuation becomes more systematic and efficient. 1. Introduction Commercial buildings such as shopping complexes have a greater risk of indoor fire according to its features such as complex structure and diverse functions [1, 2]. Early in the fire initial phase, human behavior is of utmost importance as to survival [3, 4]. The human behavior can be defined as sets of actions taken based on individuals’ perception of a situation, intention to act, as well as preaction considerations [5]. Since there is a matter of death and life in the face of fire, the likelihood of a safe escape is of central importance in buildings’ fire safety features. However, the occupants have to lean on themselves or be saved from danger by others prior to building’ fire safety features in the initial phase of a fire [6]. Therefore, other professional emergency assistance comes later. Accordingly, there is need to elucidate how individuals behave in the face of a fire. The human behavior considering how they behave during an escape can be articulated as “evacuation behavior.” Recent studies in this sphere have shown that humans’ perception based on a situation along with an action performed is the consequence of decision-making/behavioral process [7] compared to a stimulus-response to any change in the environment per se [8]. Studies from building fire and community evacuations during disasters [9–15] have indicated that research in evacuation behavior sphere is at the pivotal point of its history, and studies have discerned confident signal to act, interpreted the situation, assessed the risk, and then made a decision accordingly [16]. From processual vantage point, some certain constituents might influence each stage of above-mentioned processes. Evacuation is an effective emergency response that protects human population from the catastrophic events and unexpected disaster. Evacuation models and interpretations in safety science have been extensively studied, covering topics of calculation and estimation of evacuation time. However, there is a lack of theory and 2 accessible data on human behavior for use by generated evacuation models [17]. Human behavior in a fire has been extensively studied from various perspectives as summarized in Table 1. Although many studies have been extensively conducted in this field, some assumptions about the existing paradigm of fire safety, particularly in building area, are not consistent with knowledge generated in the literature [18, 19]. Therefore, there is a substantial need for incorporating new scientific heuristic/metaheuristic knowledge, which can be supplemented. The key for fire evacuation success is a wellplanned route [20]. The evacuation process is challenging as the situation becomes unorganized and chaotic when fire happens in a building. It is filled with uncertainties where the people affected often lack a complete picture of hazards and potential escaping route. Experimental studies indicate that 38% to 77% in the visibility of the signage could be highlighted comparing dynamic signage systems to the conventional static emergency signage ones [21]. For example, a survey studied occupants in Portuguese buildings revealed that 43% occupants were unsure what action to take during the fire, 12% did not evacuate until they saw the smoke, and 25% started to evacuate once the alarm was heard [22]. The people affected need to decide which route to follow; thus the situation becomes more chaotic [16]. The survival of evacuees largely depends on the level of ease in finding an escaping route. During fire, the psychic stress level of evacuees will increase and this can consequently impair their cognitive processes and their responses to fire [20]. It is essential to determine the best locations to install escaping guidance signs and to develop a proper fire evacuation plan, which can be achieved using swarm intelligence that is well known for its effectiveness and accuracy in finding the optimum paths in many sorts of computational problems [5]. The rapid growing interests in solving optimization problems induced the trend of applying natural metaphors, where a problem given is modeled in the way that it can be handled by a classical algorithm [6]. Classical optimization techniques have imposed limitations in solving operational problems because they depend on the type of variables used in modeling and the constraint functions such as nonlinear or linear equations [7]. The efficiency of classical optimization algorithms also largely depends on the number of variables, solution size, and structure of solution spaces; thus they are inefficient in solving large-scale and highly nonlinear or combinatorial problems [7]. Besides, many assumptions are required on the original parameters such as rounding variables and softening constraints, but in many situat (...truncated)