Proposed CAEva Simulation Method for Evacuation of People from a Buildings on Fire

Czerniak, Jacek M.; Apiecionek, Łukasz; Zarzycki, Hubert; Ewald, Dawid

doi:10.1007/978-3-319-26211-6_27

Cited by 21 publications

(6 citation statements)

References 15 publications

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“…CAEva simulation method is a program prepared for the purpose of rehearsing the fire escape scenarios in buildings [7]. It helps to compare various simulation results and to draw suitable conclusions.…”

Section: Caeva Simulation Methodmentioning

confidence: 99%

“…Such simulations can be based on the well-known "Game of Life" [18]. By introducing some modification to the algorithm, it is possible to monitor the behavior of the surrounding cells [7]. Other examples of cellular automata implementations include image processing, generation of textures, simulation of waves, wind, and people evacuation process as well as a simulation program, developed for the purpose of this study [19][20][21][22][23].…”

Section: Application Of Cellular Automatamentioning

confidence: 99%

“…With the development of computers and software, optimizing methods based on this approach have been more and more frequently studied and implemented in practice [5]. Due to their versatility, cellular automata are applied in many real-life fields, such as biology, physics, and mathematics and in various fields of IT, such as cryptography or computer graphics [6,7].…”

Section: Introductionmentioning

confidence: 99%

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A Cellular Automata-Based Simulation Tool for Real Fire Accident Prevention

Czerniak

Zarzycki

Apiecionek

et al. 2018

Mathematical Problems in Engineering

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Many serious real-life problems could be simulated using cellular automata theory. There were a lot of fires in public places which kill many people. Proposed method, called Cellular Automata Evaluation (CAEva in short), is using cellular automata theory and could be used for checking buildings conditions for fire accident. The tests performed on real accident showed that an appropriately configured program allows obtaining a realistic simulation of human evacuation. The authors analyze some real accidents and proved that CAEva method appears as a very promising solution, especially in the cases of building renovations or temporary unavailability of escape routes.

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Section: Caeva Simulation Methodmentioning

confidence: 99%

Section: Application Of Cellular Automatamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Cellular Automata-Based Simulation Tool for Real Fire Accident Prevention

Czerniak

Zarzycki

Apiecionek

et al. 2018

Mathematical Problems in Engineering

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“…Particle swarm optimization was created thanks to studies on, among others, sandblasting of a car body or other corroded metal parts. Hence, generally, this branch of AI has been called swarm intelligence [11,14,25,38]. Conversion of those intelligence mechanisms prevailing among simple individuals into the field of computer systems resulted in creation of the current sometimes called computational swarm intelligence.…”

Section: Introductionmentioning

confidence: 99%

“…But it is not necessarily the case as regards linking those changes with a trend [39,41,42]. This might be the opportunity to apply generalizations of fuzzy logic which are, in the opinion of authors of that concept, W. Kosiński [9][10][11] and his team [12,13], Ordered Fuzzy Number (OFN) [28,33,40]. There are already interesting studies available published by well-known scientists [1,18] that present successful implementation of fuzzy logic to swarm intelligence methods, including methods inspired by ant and termite colonies.…”

Section: Introductionmentioning

confidence: 99%

OFNAnt Method Based on TSP Ant Colony Optimization

Czerniak

2017

Studies in Fuzziness and Soft Computing

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This chapter presents a hybrid method of swarm intelligence current. Intelligence represented by ant colonies has been enriched with fuzzy logic arithmetics. In this case Kosiński's Ordered Fuzzy Numbers were specifically used. Apart from a fuzzy decision model of a single ant used earlier by other researchers, the author used the order as a trend support. By associating the direction of a number in Ordered Fuzzy Numbers (OFNs) with the trend observed in the ant colony it is possible to provide a unique description of a fuzzy observation of a colony behavior. The experiments were carried out in the area of searching for the optimal connecting route in the field. The experiment covered 10 complex issues of searching for the optimal route. All are benchmarks from the TSPlib repository which are well known among researchers. They represent the actual problems of route selection such as transport connections depending on geographic conditions and optimizing the machining process or the layout of the power networks. The complexity level of optimal solutions for problems to be solved amounted from several hundred to several thousand connections. Each of them was solved using six swarm intelligence methods and five well-known classical methods dedicated to the traveling salesman problem (TSP). The results were presented in the form of tables and graphs, and some of the routes were shown in graphical form. Final conclusions of the experiment indicate the superiority of methods based on ant colony optimization as regards closeness to optimal solutions. The results achieved by the OFNAnt method are generally better (in 92% of cases) than those achieved by classic methods and are in the forefront of solutions from the swarm intelligence group.

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