An emergency evacuation path planning method based on HBA optimized ant colony algorithm

Abstract

The present invention discloses an emergency evacuation path planning method based on HBA optimized ant colony algorithm. First, based on the constructed two-dimensional map of indoor space, the present invention initializes the number and position of the population, updates the density factor and obtains the initial optimal path trajectory x based on the honey badger optimization algorithm. new ; Then use the obtained initial path trajectory x new The ant colony algorithm's initial pheromone is optimized; distance estimation and smoothing functions are then introduced to improve the algorithm's transition probability selection; finally, the ant colony algorithm's pheromone is updated, and after iteration, the optimal path Z is output. This invention introduces the honey badger optimization algorithm into the ant colony algorithm, further enhancing the algorithm's rapid search capabilities and avoiding the problem of being trapped in local optimality. Furthermore, an improved heuristic function is proposed by combining smoothing functions and distance estimation to further increase the algorithm's convergence speed. This method is adaptable to path planning in complex scenarios and improves evacuation efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency management, and in particular relates to an emergency evacuation path planning method based on HBA optimized ant colony algorithm. Background Art

[0002] With the rapid development of social economy, various complex buildings such as transportation hubs and large complexes are increasing, such as subway environments, airport terminals, large shopping malls, etc. However, once an emergency occurs, such as a fire or a criminal attack, it will cause great harm to the safety of people. Therefore, more and more researchers are conducting research on reasonable evacuation planning paths under emergencies. At present, software such as Pathfinder is generally used to carry out emergency evacuation simulations. Although it can simulate evacuation scenarios to a certain extent, due to the complexity of the actual evacuation environment, the uncontrollability of personnel, and the relatively simple evacuation models constructed by these software, they cannot fit the actual evacuation scenarios. Their use in specific evacuation processes is limited and needs further research.

[0003] In recent years, the emergence of intelligent optimization algorithms, including particle swarm optimization (PSO), ant colony optimization (ACO), and sparrow search algorithms, has provided new solutions for indoor emergency evacuation path planning during emergencies. Among these optimization algorithms, the ACO has been widely used due to its strong global search capabilities and parallel search capabilities. However, in actual emergency evacuation processes, traditional ACO suffers from low search efficiency, a large number of inflection points, and long path lengths. Summary of the Invention

[0004] In response to the above-mentioned problems, the present invention proposes an emergency evacuation path planning method based on the HBA optimized ant colony algorithm. First, in order to address the problem that the initial pheromone distribution of the ant colony algorithm is unreasonable, which makes it easy to fall into the local optimum, the present invention introduces the honey badger optimization algorithm into the ant colony algorithm. The initial pheromone concentration required by the ant colony algorithm is obtained by the rapid optimization ability of the honey badger optimization algorithm, thereby avoiding the problem of the ant colony algorithm falling into the local optimum. Secondly, the present invention uses a Manhattan-based distance estimation method on the basis of the traditional ant colony algorithm, thereby guiding the ant colony algorithm to search in the direction of the shortest target. Finally, based on the above method, a smoothing function is designed to minimize the number of turns during the evacuation process and improve the stability and safety of the evacuation route planning. Compared with the traditional emergency evacuation path planning method based on the ant colony algorithm, the method of the present invention can improve the efficiency and quality of planning, thereby helping people to quickly complete the evacuation process.

[0005] The above purpose is achieved through the following technical solutions:

[0006] An emergency evacuation path planning method based on HBA optimized ant colony algorithm, the method comprising the following steps:

[0007] S1. Construct a two-dimensional map of the indoor space, including indoor obstacles, evacuation routes, evacuation exits, and emergency danger zones;

[0008] S2. Based on the indoor space two-dimensional map constructed in step S1, the number and position of the population are initialized, the density factor is updated, and the initial optimal path trajectory x is obtained based on the honey badger optimization algorithm. new ;

[0009] S3. Use the initial path trajectory x obtained in step S2 new Optimize the initial pheromone of the ant colony algorithm;

[0010] S4. Introducing distance estimation and smoothing function to improve the transition probability selection of ant colony algorithm;

[0011] S5. Pheromone update of ant colony algorithm;

[0012] S6. After iteration, the optimal path Z is generated and output.

[0013] Furthermore, step S2 includes the following sub-steps:

[0014] S2-1. Based on the honey badger optimization algorithm, initialize the number and location of the population. The specific formula is as follows:

[0015] x n =lb n +r1×(ub n -lb n )

[0016] where x n Indicates the position of the nth honey badger, lb n Indicates the lower bound of the search area of ​​the nth honey badger, ub n represents the upper bound of the search area of ​​the nth honey badger, and r1 represents a random number between 0 and 1;

[0017] S2-2. First, the density factor expression formula is given as follows:

[0018]

[0019] S=(x n -x n+1 ) 2

[0020] d n =x prey -x n

[0021] Among them, I nrepresents the olfactory intensity of the nth honey badger, r2 is a random number (0, 1), S is the concentration intensity between the honey badger and the prey, d n represents the Euclidean distance between the nth honey badger and its prey, x prey In mathematical formulas, it represents the current global optimal solution, which is used to simulate the optimal position of prey in the honey badger optimization algorithm. n+1 Indicates the position of the n+1th honey badger;

[0022] Then give the following density factor update formula:

[0023]

[0024] Where α represents the density factor; C = 2 represents a constant; NM represents the number of iterations; NM max represents the maximum number of iterations; exp(·) represents the exponential function;

[0025] S2-3. During the mining phase, the following formula is satisfied:

[0026] x new =x prey +F×β×I×x prey +F×r3×α×d n ×|cos(2πr4)×[1-cos(2πr5)]|

[0027] Among them, x new Indicates the new position of the honey badger, x prey represents the optimal position of the prey, β = 6 represents the predation ability of the honey badger, and F is the search direction, which is used to provide the scanning space. The specific expression is as follows:

[0028]

[0029] Among them, r6 is a random number between 0 and 1;

[0030] S2-4. During the honey collection phase, the trajectory of the honey badger following the honey guide bird to the nectar source satisfies the following formula:

[0031] x new =x prey +F×r7×α×d n

[0032] Where r7 represents a random number and α represents a density factor.

[0033] Furthermore, the initial pheromone of the ant colony algorithm is optimized in step S3, and the optimized initial pheromone is expressed as follows:

[0034] τ ij (x new )=XR

[0035] Among them, τ ij (x new ) represents the initialization pheromone, X=1.5 is the adjustment constant; R=0.3 represents the pheromone on other paths.

[0036] Furthermore, the distance estimation and smoothing function are introduced in step S4 to improve the transition probability selection of the ant colony algorithm. The improved transition probability selection The formula is as follows:

[0037]

[0038] Where k represents the selectable node; l represents the heuristic factor of pheromone; m represents the heuristic factor function; τ ij (t) represents the pheromone from the ant from node i to node j at time t; A k represents the set of nodes that the ant can select; η ij (t) represents the heuristic function of the ant from node i to node j at time t; ij (t) represents the introduced smoothing function; and:

[0039]

[0040] Among them, d j is the distance from node j to the target point, ε is the balance factor, d si is the distance from the starting point s to the node i, d ij represents the distance from node i to node j, d sj Indicates the distance from the starting point s to the node j, N c and N q is the number of iterations;

[0041]

[0042] Where θ = 0.8 represents the smoothness percentage; dir ij (t) represents the transfer direction from the current node i to the next node j at time t; dir iv (t) represents the transfer direction from the previous node v to the current node i at time t.

[0043] Furthermore, the expression for pheromone update in step S5 is as follows:

[0044] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t)

[0045]

[0046] Where ρ represents the pheromone value; Δτ ij (t) is the pheromone increment when the ant passes through the path coordinate (i, j) at time t; U is the pheromone reinforcement coefficient; L k is the total length of the ant's path from the starting point to the destination.

[0047] The beneficial effects of the present invention compared to the prior art are:

[0048] (1) Aiming at the problem that the ant colony algorithm is prone to falling into local optimality, the present invention introduces the honey badger optimization algorithm into the ant colony algorithm, thereby avoiding the problem that the ant colony algorithm falls into local optimality.

[0049] (2) The present invention uses a Manhattan-based distance estimation method based on the traditional ant colony algorithm to guide the ant colony algorithm to search in the direction of the shortest target;

[0050] (3) The present invention designs a smoothing function to minimize the number of turns during the evacuation process and improve the stability and safety of evacuation route planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flow chart for implementing the method of the present invention;

[0052] Figure 2 This is the optimization result of the personnel evacuation path of the traditional fireworks algorithm;

[0053] Figure 3 The optimization result of the personnel evacuation path by the method of the present invention;

[0054] Figure 4 The graph shows the comparison of the optimization results of the traditional fireworks algorithm and the method of the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described below with reference to the accompanying drawings and specific examples.

[0056] like Figure 1 As shown in the figure, in a known indoor space, when an emergency occurs, people need to plan an escape route as quickly as possible to leave the danger zone in the shortest possible time. The present invention provides an emergency evacuation path planning method based on the HBA optimized ant colony algorithm, which enables evacuees to quickly escape the danger zone according to the route calculated by the system. The specific implementation steps are as follows:

[0057] Step 1: Update Honey Badger optimization algorithm

[0058] ① Construct a two-dimensional map of the indoor space, including information such as indoor obstacles, evacuation passages, evacuation exits, and emergency danger areas.

[0059] ② Based on the above map, the number and location of the population are initialized based on the honey badger optimization algorithm. The specific formula is as follows:

[0060] x n =lb n +r1×(ub n -lb n )

[0061] where x n Indicates the position of the nth honey badger, lb n Indicates the lower bound of the search area of ​​the nth honey badger, ub n It represents the upper bound of the search area of ​​the nth honey badger, and r1 represents a random number between 0 and 1.

[0062] ③ Give and update the density factor

[0063] In the honey badger optimization algorithm, it is considered that the greater the olfactory intensity, the faster the honey badger's movement speed, and the smaller the olfactory intensity, the slower the honey badger's movement speed. Therefore, the present invention provides the density factor related expression formula as follows:

[0064]

[0065] S=(x n -x n+1 ) 2

[0066] d n =x prey -x n

[0067] Among them, I n represents the olfactory intensity of the nth honey badger, r2 is a random number in (0,1), S is the concentration intensity between the honey badger and the prey, d n represents the Euclidean distance between the nth honey badger and its prey, x prey In mathematical formulas, it represents the current global optimal solution, which is used to simulate the optimal position of prey in the honey badger optimization algorithm. n+1 Indicates the position of the n+1th honey badger.

[0068] Since the density factor changes over time, the present invention provides the following density factor update formula:

[0069]

[0070] Where α represents the density factor; C = 2 represents a constant; NM represents the number of iterations; NM max represents the maximum number of iterations; exp(·) represents the exponential function.

[0071] ④ Excavation stage

[0072] In the mining phase, the following formula is satisfied:

[0073] x new =x prey +F×β×I×x prey +F×r3×α×d n ×|cos(2πr4)×[1-cos(2πr5)]|

[0074] Among them, x new Indicates the new position of the honey badger, x prey represents the optimal position of the prey, β = 6 represents the predation ability of the honey badger, and F is the search direction, which is used to provide the scanning space. The specific expression is as follows:

[0075]

[0076] Here, r6 is a random number between 0 and 1.

[0077] ⑤ Honey collecting stage

[0078] The trajectory of the honey badger following the honey-guided bird to the nectar source satisfies the following formula:

[0079] x new =x prey +F×r7×α×d n

[0080] Where r7 represents a random number and α represents a density factor.

[0081] Step 2: Ant Colony Algorithm Initial Pheromone Optimization Processing

[0082] The present invention uses the honey badger optimization algorithm to update the results to optimize the initial pheromone of the ant colony algorithm. Through the above steps, an optimal path trajectory x has been obtained. new At this time, the pheromone on this path is set higher than that on other paths to give full play to the guiding role of the honey badger optimization algorithm in the early stage of the ant colony algorithm. This will avoid falling into the local optimum and improve the convergence speed of the algorithm. The optimized initial pheromone is expressed as follows:

[0083] τ ij (x new )=XR

[0084] Among them, τ ij (x new ) represents the initialization pheromone, X=1.5 is the adjustment constant; R=0.3 represents the pheromone on other paths.

[0085] Step 3: Improved transition probability selection

[0086] The traditional ant colony algorithm selects the next position according to the grid and obstacles during the search process. The roulette method is generally used to realize the probability of moving from node i to node t at time t. The present invention introduces distance estimation and smoothing function on the basis of the traditional ant colony algorithm transfer probability selection formula. The improved transfer probability selection formula is: The formula is as follows:

[0087]

[0088] Where k represents the selectable node; l represents the heuristic factor of pheromone; m represents the heuristic factor function; τ ij (t) represents the pheromone from the ant from node i to node j at time t; A k represents the set of nodes that the ant can select; η ij (t) represents the heuristic function of the ant from node i to node j at time t; ij (t) represents the introduced smoothing function.

[0089] The following is an explanation of the two improvements of the present invention mentioned above.

[0090] ① where η ij (t) is the heuristic function for distance estimation introduced in the present invention. The estimated distance is based on the Manhattan distance. Based on the introduction of the estimated value, a balancing factor is added to accelerate the convergence of the algorithm. The improved heuristic function of the present invention is expressed as follows:

[0091]

[0092] Among them, d j is the distance from node j to the target point, ε is the balance factor, d si is the distance from the starting point s to the node i, d ij represents the distance from node i to node j, d sj Indicates the distance from the starting point s to the node j, N c and N q is the number of iterations.

[0093] ②λ ij (t) represents the smoothing function introduced in the present invention, which is as follows:

[0094] Considering the safety factors during the evacuation process, the number of turns is minimized. The present invention adds a smoothing function λ ij (t), improve the stability of the algorithm and the safety of the evacuation process, specifically expressed as follows:

[0095]

[0096] Where θ = 0.8 represents the smoothness percentage; dirij (t) represents the transfer direction from the current node i to the next node j at time t; dir iv (t) represents the transfer direction from the previous node v to the current node i at time t.

[0097] Step 4: Pheromone Update

[0098] Here we use the pheromone update expression in the traditional ant colony algorithm as follows:

[0099] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t)

[0100]

[0101] Where ρ represents the pheromone value; Δτ ij (t) is the pheromone increment when the ant passes through the path coordinate (i, j) at time t; U is the pheromone reinforcement coefficient; L k is the total length of the ant's path from the starting point to the destination.

[0102] Step 5: Optimal path output

[0103] In the above continuous iterative process, the optimal path Z is finally formed.

[0104] Experimental verification:

[0105] To verify the effectiveness of the proposed emergency evacuation path planning method based on the HBA-optimized ant colony algorithm, experiments were designed. In the experiment, a grid area with a certain number of irregular obstacles was set as the evacuation space. The evacuation starting point was the upper left corner of the grid area, and the evacuation exit / end point was located in the lower right corner of the grid area. Under these conditions, evacuation path planning experiments were conducted using both a traditional ant colony algorithm and the proposed method.

[0106] result: Figure 1 Flowchart of implementation of the method of the present invention. Figure 2 Emergency evacuation route planning based on traditional ant colony algorithm. Figure 3 The method of the present invention provides an emergency evacuation route planning method. Figure 4 Comparison curve of path lengths planned by different methods. Figure 4It can be seen that the method of the present invention achieves the shortest path length with the least number of iterations. The traditional ant colony algorithm takes 5.176471 seconds to complete the entire route planning process, while the method of the present invention takes 4.784792 seconds, demonstrating its higher efficiency. In terms of average path length, the traditional ant colony algorithm plans a path length of 34.0933 meters, while the method of the present invention plans a path length of 29.9120 meters, demonstrating that the planning of the present invention is more reasonable.

Claims

1. An emergency evacuation path planning method based on HBA optimized ant colony algorithm, characterized in that: The method comprises the following steps: S1. Construct a two-dimensional map of the indoor space, including indoor obstacles, evacuation routes, evacuation exits, and emergency danger zones; S2. Based on the indoor space two-dimensional map constructed in step S1, the number and position of the population are initialized, the density factor is updated, and the initial optimal path trajectory x is obtained based on the honey badger optimization algorithm. new ; S3. Use the initial path trajectory x obtained in step S2 new Optimize the initial pheromone of the ant colony algorithm; S4. Introducing distance estimation and smoothing function to improve the transition probability selection of ant colony algorithm; S5. Pheromone update of ant colony algorithm; S6. After iteration, the optimal path Z is generated and output; Step S2 includes the following sub-steps: S2-1. Based on the honey badger optimization algorithm, initialize the number and location of the population. The specific formula is as follows: x n =lb n +r1×(ub n -lb n ) where x n Indicates the position of the nth honey badger, lb n Indicates the lower bound of the search area of ​​the nth honey badger, ub n represents the upper bound of the search area of ​​the nth honey badger, and r1 represents a random number between 0 and 1; S2-2. First, the density factor expression formula is given as follows: S=(x n -x n+1 ) 2 d n =x prey -x n Among them, I n represents the olfactory intensity of the nth honey badger, r2 is a random number in (0,1), S is the concentration intensity between the honey badger and the prey, d n represents the Euclidean distance between the nth honey badger and its prey, x prey In mathematical formulas, it represents the current global optimal solution, which is used to simulate the optimal position of prey in the honey badger optimization algorithm. n+1 Indicates the position of the n+1th honey badger; Then give the following density factor update formula: Where ɑ represents the density factor; C = 2 represents a constant; NM represents the number of iterations; NM max represents the maximum number of iterations; exp(·) represents the exponential function; S2-3. During the mining phase, the following formula is satisfied: x new =x prey +F×β×I×x prey +F×r3×ɑ×d n ×|cos(2πr4)×[1-cos(2πr5)]| Among them, x new Indicates the new position of the honey badger, x prey represents the optimal position of the prey, β = 6 represents the predation ability of the honey badger, and F is the search direction, which is used to provide the scanning space. The specific expression is as follows: Among them, r6 is a random number between 0 and 1; S2-4. During the honey collection phase, the trajectory of the honey badger following the honey guide bird to the nectar source satisfies the following formula: x new =x prey +F×r7×α×d n Where r7 represents a random number and α represents a density factor.

2. The method for emergency evacuation path planning based on HBA optimized ant colony algorithm according to claim 1, characterized in that: The initial pheromone of the ant colony algorithm is optimized in step S3. The optimized initial pheromone is expressed as follows: τ ij (x new )=XR Among them, τ ij (x new ) represents the initialization pheromone, X=1.5 is the adjustment constant; R=0.3 represents the pheromone on other paths.

3. The method for emergency evacuation path planning based on HBA optimized ant colony algorithm according to claim 2, characterized in that: In step S4, the distance estimation and smoothing function are introduced to improve the transition probability selection of the ant colony algorithm. The improved transition probability selection The formula is as follows: Where k represents the selectable node; l represents the heuristic factor of pheromone; m represents the heuristic factor function; τ ij (t) represents the pheromone from the ant from node i to node j at time t; A k represents the set of nodes that the ant can select; η ij (t) represents the heuristic function of the ant from node i to node j at time t; ij (t) represents the introduced smoothing function; and: Among them, d j is the distance from node j to the target point, ε is the balance factor, d si is the distance from the starting point s to the node i, d ij represents the distance from node i to node j, d sj Indicates the distance from the starting point s to the node j, N c and N q is the number of iterations; Among them, 0 = 0.8 represents the smoothness percentage; dir ij (t) represents the transfer direction from the current node i to the next node j at time t; dir iv (t) represents the transfer direction from the previous node v to the current node i at time t.

4. The method for emergency evacuation path planning based on HBA optimized ant colony algorithm according to claim 3, characterized in that: The expression for pheromone update in step S5 is as follows: t ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t) Where ρ represents the pheromone value; Δτ ij (t) is the pheromone increment when the ant passes through the path coordinate (i, j) at time t; U is the pheromone reinforcement coefficient; L k is the total length of the ant's path from the starting point to the destination.

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