A Comprehensive Study on Pedestrians’ Evacuation

Muhammed, Danial A.; Saeed, Soran; Rashid, Tarik A.

doi:10.3991/ijes.v7i4.11767

“…Also, it can be seen as a real-world application and will be optimized by different optimizer algorithms, such as fitness dependent optimizer (FDO) [32], WOA-BAT optimization [33], donkey and smuggler optimization [34], and modified grey wolf optimizer [35] to find best location of the main exit door through the area of evacuation in a building. Finally, interested readers can read references [36][37][38] for the possibility of obtaining future directions.…”

Section: Resultsmentioning

confidence: 99%

A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques

Muhammed¹,

Saeed²,

Rashid³

2019

RIA

View full text Add to dashboard Cite

Cite as: Muhammed, D.A., Saeed, S.A.M., Rashid, T.A. (2019). A simulation model for pedestrian crowd evacuation based on various AI techniques. ABSTRACTThis paper attempts to design an intelligent simulation model for pedestrian crowd evacuation. For this purpose, the cellular automata (CA) was fully integrated with fuzzy logic, the kth nearest neighbors (KNN), and some statistical equations. In this model, each pedestrian was assigned a specific speed, according to his/her physical, biological and emotional features. The emergency behavior and evacuation efficiency of each pedestrian were evaluated by coupling his/her speed with various elements, such as environment, pedestrian distribution and familiarity with the exits. These elements all have great impacts on the evacuation process. Several experiments were carried out to verify the performance of the model in different emergency scenarios. The results show that the proposed model can predict the evacuation time and emergency behavior in various types of building interiors and pedestrian distributions. The research provides a good reference to the design of building evacuation systems.

show abstract

“…Consequently, from both the IFDO and FDO results, it clearly appears that the IFDO is better for optimizing this problem due to its increasing capability of making better decisions in exploring better hives among the existing potential hives by adding alignment and cohesion when the scout wants to go to a different location in the defined space search; it also avoids unsuitable exploitation in achieving a better solution when, for every achieved better solution, the IFDO generates a new to control the (see the pseudocode in Figure (1)). In the last two decades, scenarios involving the evacuation of crowds and pedestrians have been studied in many works to reduce the negative aspects of emergency situations, such as deaths, damages, and injuries [63]. In this part of this paper, we create a simple pedestrian evacuation model based on a cellular automata model (see Figure (8)), fuzzy logic ideas, and statistical equations.…”

Section: A-use Of the Ifdo On Aperiodic Antenna Array Designsmentioning

confidence: 99%

Improved Fitness-Dependent Optimizer Algorithm

Muhammed¹,

Saeed²,

Rashid³

2020

Preprint

View full text Add to dashboard Cite

<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

show abstract

“…Consequently, from both the IFDO and FDO results, it clearly appears that the IFDO is better for optimizing this problem due to its increasing capability of making better decisions in exploring better hives among the existing potential hives by adding alignment and cohesion when the scout wants to go to a different location in the defined space search; it also avoids unsuitable exploitation VOLUME XX, 2017 in achieving a better solution when, for every achieved better solution, the IFDO generates a new to control the (see the pseudocode in Figure (1)). In the last two decades, scenarios involving the evacuation of crowds and pedestrians have been studied in many works to reduce the negative aspects of emergency situations, such as deaths, damages, and injuries [63]. In this part of this paper, we create a simple pedestrian evacuation model based on a cellular automata model (see Figure (8)), fuzzy logic ideas, and statistical equations.…”

Section: ) Real World Applications Of the Ifdomentioning

confidence: 99%

Improved Fitness-Dependent Optimizer Algorithm

Muhammed¹,

Saeed²,

Rashid³

2020

Preprint

0

View full text Add to dashboard Cite

<div> <table> <tr> <td> <p>The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor ( ), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.</p> </td> </tr> </table> </div> <br>

show abstract

A Comprehensive Study on Pedestrians’ Evacuation

Cited by 7 publications

References 99 publications

A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques

A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques

Improved Fitness-Dependent Optimizer Algorithm

Improved Fitness-Dependent Optimizer Algorithm

Contact Info

Product

Resources

About