Method for optimizing evacuation path of personnel under emergency based on optimization algorithm

Abstract

The application discloses a method for optimizing evacuation path of personnel under emergency based on an optimization algorithm. First, an indoor emergency evacuation map is established to generate a grid evacuation environment, and the Cubic chaotic mapping is introduced into the firework population initialization in the grid evacuation environment. The number of exploding fireworks is calculated on the basis of the initialized firework population. Then, the firework explosion range is calculated based on the Piecewise chaotic method. The position of the firework is subjected to a free mutation operation. Next, the optimal individual position is selected by using a hybrid selection strategy combining the elite selection and the roulette strategy. Finally, the optimal path is formed by saving the optimal individual position. The free mutation operation and the hybrid selection strategy are designed, wherein the hybrid strategy is realized by using the roulette strategy and the elite selection in a mixed weighting manner. Compared with the traditional firework algorithm, the application is more suitable for the evacuation environment in actual public places, and can further improve the evacuation path optimization efficiency and accuracy.

Description

Technical Field

[0001] This invention belongs to the field of emergency management technology, specifically relating to a method for optimizing personnel evacuation routes under emergencies based on optimization algorithms. Background Technology

[0002] Emergency evacuation refers to the rapid guidance of people to safe areas in the event of a sudden incident. When an emergency occurs in a public place, it is necessary to quickly guide people to evacuation areas, making accurate and efficient evacuation route optimization particularly important. In traditional evacuation scenarios, evacuees complete the evacuation by observing evacuation signs and exit indicators. However, this method is inefficient and the route planning is unreasonable. Therefore, it is necessary to explore and research personnel evacuation route optimization methods based on optimization algorithms, incorporating more information.

[0003] With the continuous improvement of optimization algorithms, new intelligent optimization algorithms are constantly emerging, providing an opportunity to solve the problem of optimizing evacuation routes in emergency situations. Among the many intelligent optimization algorithms, the fireworks algorithm has been widely used due to its strong global search capability and parallel search characteristics. However, considering the complexity of actual evacuation scenarios, the traditional fireworks algorithm suffers from problems such as weak local optimization ability, low search efficiency, and long optimization paths. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a method for optimizing evacuation routes during emergencies based on an optimization algorithm. First, this invention introduces Cubic chaotic mapping into the initialization of the fireworks algorithm, designing a new initialization formula that makes the initial spatial distribution more reasonable and uniform, avoiding getting trapped in local optima. Second, addressing the issue that traditional fireworks algorithms do not consider obstacle information, this invention incorporates an adaptive factor into the algorithm's fitness formula to effectively avoid obstacles and improve the accuracy of route optimization during emergency evacuations. Next, this invention introduces the Piecewise chaotic method to calculate the fireworks explosion range, improving the algorithm's global search accuracy and avoiding getting trapped in local optima. Finally, to prevent the algorithm from getting trapped in the optimal starting region, this invention designs a free mutation operation and a hybrid selection strategy, where the hybrid strategy is implemented using a weighted combination of roulette wheel selection and elite selection. Compared to traditional fireworks algorithms, this invention is more closely aligned with the actual evacuation environment in public places, further improving the efficiency and accuracy of evacuation route optimization.

[0005] The above objectives are achieved through the following technical solutions:

[0006] A method for optimizing evacuation routes during emergencies based on optimization algorithms, the method includes the following steps:

[0007] Step 1: Establish an indoor emergency evacuation map, generate a gridded evacuation environment, and introduce Cubic chaotic mapping into the fireworks population initialization within the gridded evacuation environment.

[0008] Step 2: Calculate the number of exploding fireworks based on the initialization of the fireworks population in Step 1;

[0009] Step 3: Calculate the explosion range of fireworks based on the Piecewise chaos method;

[0010] Step 4: Perform free variation operation on the position of the fireworks;

[0011] Step 5: Use a hybrid selection strategy that combines elite selection and roulette wheel selection to select the optimal individual position;

[0012] Step Six: Save the optimal individual position obtained in Step Five. Finally, the optimal path is formed.

[0013] Furthermore, the specific operations of step one include the following sub-steps:

[0014] Sub-step 1.1: Obtain a two-dimensional planar map of the interior layout, divide it into grids, and generate a gridded evacuation environment;

[0015] Sub-step 1.2: Introduce the Cubic chaotic mapping into the fireworks initialization:

[0016]

[0017] in The first Cubic chaos is represented by the... A sequence; The first Cubic chaos is represented by the... A sequence; Indicates the number of sequences; Represents the chaos coefficient;

[0018] Sub-step 1.3, Fireworks Population Initialization: Based on the sequence generated by the Cubic chaotic mapping in sub-step 1.2, transform it into the search space of the fireworks population to generate the initial fireworks population:

[0019]

[0020] in, Indicates the first Initialize a fireworks population; This represents the initial minimum value of the fireworks population; This indicates the maximum value of the initial fireworks population.

[0021] Furthermore, the calculation of the number of exploding fireworks in step two is expressed using a fitness function based on an adaptive factor, as follows:

[0022]

[0023] in, This indicates that the previous generation of fireworks produced offspring fireworks. The x-coordinate; This indicates that the previous generation of fireworks produced offspring fireworks. The ordinate; This indicates that the previous generation of fireworks was produced. The x-coordinate; This indicates that the previous generation of fireworks was produced. The ordinate; Indicates individual fireworks The fitness function value; The fireworks species are represented, and the specific calculations are as follows:

[0024]

[0025] Based on the above fitness function values, we obtain the first... The number of exploding fireworks produced by each firework as follows:

[0026]

[0027] In the formula, Indicates the first Fireworks location; The total number of fireworks; This indicates the maximum adaptability value of the fireworks; =5 represents a constant. Indicates individual fireworks The fitness function value.

[0028] Furthermore, the calculation of the fireworks explosion range based on the Piecewise chaotic method described in step three is expressed as a piecewise function:

[0029]

[0030] in, This represents the explosion amplitude of the i-th firework. In the formula, Indicates the location of the maximum explosion; This represents the minimum fitness value for fireworks. Indicates the radius of the fireworks blast; exist The value within the function is a piecewise control factor used to divide the piecewise function into four parts.

[0031] Based on the above-mentioned number of fireworks and explosion range, the position update formula for the i-th firework in the z-direction is as follows:

[0032]

[0033] In the formula, This represents the position value of the i-th firework in the z-direction. This represents the Gaussian variation value of the fireworks at the k-th dimension. The selected dimension; For interval A uniformly random number.

[0034] Furthermore, the strategy for freely varying the position of the fireworks described in step four is as follows:

[0035]

[0036] in, This indicates the position of the fireworks with the optimal adaptive value, also known as the optimal position. This indicates the current optimal firework is at the [number]th position. Position in dimensions; This indicates that the worst fireworks display is currently in the [number] category. Position in dimensions; Indicates the interval Random numbers that are uniformly distributed.

[0037] Furthermore, the probability of an individual being selected under the hybrid selection strategy described in step five is expressed as follows:

[0038]

[0039] in, Indicates individual fireworks The probability of being selected. This represents a constant used to adjust probability values. This represents the minimum fitness value for fireworks. It is a very small parameter that prevents division by zero.

[0040] The advantages of this invention compared to the prior art are:

[0041] (1) To address the problem of uneven initial distribution that easily leads to local optima, this invention introduces Cubic chaotic mapping into the initialization of the fireworks algorithm and designs a new initialization formula to make the distribution in the initial space more reasonable and uniform and avoid falling into local optima.

[0042] (2) In view of the problem that traditional fireworks algorithms do not consider obstacle information, this invention designs an adaptive factor in the fitness formula of the algorithm to effectively avoid obstacles and improve the accuracy of path optimization under emergency evacuation.

[0043] (3) This invention introduces the Piecewise chaos method to calculate the range of fireworks explosion, improves the global search accuracy of the algorithm and avoids getting trapped in local optima. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention;

[0045] Figure 2 The results of optimizing personnel evacuation routes using traditional fireworks algorithms;

[0046] Figure 3 This is the result of optimizing the personnel evacuation routes using the method of the present invention;

[0047] Figure 4 The curves show a comparison of the optimization results of the traditional fireworks algorithm and the method of this invention. Detailed Implementation

[0048] The present invention will be further described below with reference to the accompanying drawings and specific examples.

[0049] like Figure 1 As shown, the present invention provides a method for optimizing evacuation routes during emergencies based on optimization algorithms, comprising the following steps:

[0050] Step 1: Fireworks Initialization Based on Cubic Chaotic Mapping

[0051] Create an indoor emergency evacuation map. Obtain a two-dimensional plan view of the indoor layout, divide it into grids, and generate a gridded evacuation environment.

[0052] Cubic chaotic mapping computation. Traditional fireworks algorithms typically use random functions to generate the initial fireworks population when optimizing paths in a grid environment. This method results in an uneven distribution of the initial fireworks population and poor ergodicity. Based on this, and considering that Cubic chaotic mapping has better chaotic ergodicity and generates more uniform sequences, this invention introduces Cubic chaotic mapping into fireworks initialization.

[0053]

[0054] in The first Cubic chaos is represented by the... A sequence; The first Cubic chaos is represented by the... A sequence; Indicates the number of sequences; This represents the chaos coefficient.

[0055] Fireworks population initialization. Based on the sequence generated by the Cubic chaotic mapping above, it is transformed into the search space of the fireworks population to generate an initial fireworks population:

[0056]

[0057] in, Indicates the first Initialize a fireworks population; This represents the initial minimum value of the fireworks population; This indicates the maximum value of the initial fireworks population.

[0058] Step 2: Calculation of the number of exploding fireworks based on adaptive factors

[0059] The number of exploding fireworks is calculated based on the initial fireworks population described above. Generally, only high-quality fireworks will explode. Therefore, it is necessary to find a sufficient number of high-quality fireworks. In traditional fireworks algorithms, the selection of the current evacuation path node position is primarily based on the distance between the current fireworks' fitness function and the starting position. However, in actual evacuation scenarios, obstacles such as surrounding buildings and facilities can cause changes in the path nodes. Therefore, this invention incorporates obstacle information into the traditional fireworks algorithm. Specifically, this invention adds an adaptive factor to the fitness formula of the traditional fireworks algorithm, with the aim of removing fireworks that fall on obstacles. The fitness function based on the adaptive factor is expressed as follows:

[0060]

[0061] in, This indicates that the previous generation of fireworks produced offspring fireworks. The x-coordinate; This indicates that the previous generation of fireworks produced offspring fireworks. The ordinate; This indicates that the previous generation of fireworks was produced. The x-coordinate; This indicates that the previous generation of fireworks was produced. The ordinate; Indicates individual fireworks The fitness function value; This indicates the type of fireworks and is related to the initial number of fireworks:

[0062]

[0063] The fitness function value of the algorithm determines the number of fireworks exploding. Based on the fitness function value mentioned above, the number of fireworks can be obtained. The number of exploding fireworks produced by each firework is as follows:

[0064]

[0065] In the formula, Indicates the first Fireworks location; The total number of fireworks; This indicates the maximum adaptability value of the fireworks; =5 represents a constant. Indicates individual fireworks The fitness function value.

[0066] Step 3: Calculation of the fireworks explosion range based on the Piecewise chaos method

[0067] Calculating the Explosion Range of Fireworks. This invention introduces a Piecewise chaotic method to calculate the explosion range of fireworks, improving the global search accuracy of the algorithm and avoiding getting trapped in local optima.

[0068] The explosion radius is generated using the Piecewise chaotic method, specifically as follows:

[0069]

[0070] in, This represents the explosion amplitude of the i-th firework. , Indicates the location of the maximum explosion; This represents the minimum fitness value for fireworks. Indicates the radius of the fireworks blast; exist The inner value is a piecewise control factor used to divide the piecewise function into four parts. This invention selects... The range of chaotic orbital states is... .

[0071] Based on the above-mentioned number of fireworks and explosion range, the position update formula for the i-th firework in the direction of the explosion is as follows:

[0072]

[0073] In the formula, This represents the position value of the i-th firework in the z-direction. This represents the Gaussian variation value of the fireworks at the k-th dimension. The selected dimension; For interval A uniformly random number.

[0074] Step 4: Free mutation operation

[0075] Based on the above, this invention proposes a free mutation strategy. Considering that the Gaussian mutation method used in the traditional fireworks algorithm is prone to overlap, this invention provides the following free mutation strategy:

[0076]

[0077] in, This indicates the current optimal firework is at the [number]th position. Position in dimensions; This indicates that the worst fireworks display is currently in the [number] category. Position in dimensions; Indicates the interval Random numbers that are uniformly distributed.

[0078] Step 5: Hybrid Selection Strategy

[0079] Traditional fireworks algorithms do not consider the possibility of mutations in offspring that could lead to differences in the quality of their offspring. Therefore, this invention proposes a selection strategy that combines elite selection and roulette wheel selection. The probability of an individual being selected is expressed as follows:

[0080]

[0081] in, This represents the minimum fitness value for fireworks. This represents a constant used to adjust probability values. It is a minimal parameter that prevents division by zero. This approach can further improve the algorithm's local search capability while preserving the optimal solution.

[0082] Step 6: Save the path points

[0083] Save the optimal path location Finally, the optimal path is formed.

[0084] Experimental verification:

[0085] To verify the effectiveness of the proposed optimization algorithm-based method for optimizing evacuation routes during emergencies, a personnel evacuation simulation experiment was designed. The simulated evacuation environment was an indoor room. Figure 1 This is a plan view of the evacuation environment, with the evacuation area gridded. Evacuated personnel are positioned in the upper left corner of the evacuation area, and evacuation exits are in the lower right corner. Based on this evacuation environment, an evacuation path optimization simulation experiment is conducted using both the traditional fireworks algorithm and the method of this invention.

[0086] Simulation Result Analysis: Figure 1 Flowchart of the method of this invention. Figure 2 Results of personnel evacuation path optimization using traditional fireworks algorithms. Figure 3 The method of this invention optimizes personnel evacuation routes. Figure 4 Comparison curves of the path optimization results of the two methods. Figure 4 It can be seen that the traditional fireworks algorithm takes 34.7252 seconds for the entire route optimization process, while the method of this invention takes 31.7431 seconds, indicating that the present invention is more efficient. Regarding the average path length, the optimized path length of the traditional fireworks algorithm is 34.7252 meters, while the optimized path length of the method of this invention is 31.7431 meters, demonstrating that the path optimization result of the present invention is optimal.

Claims

1. A method for optimizing evacuation routes during emergencies based on optimization algorithms, characterized in that, The method includes the following steps: Step 1: Establish an indoor emergency evacuation map, generate a grid evacuation environment, and introduce Cubic chaotic mapping into the fireworks population initialization in the grid evacuation environment. Step 2: Based on the initialization of the fireworks population in Step 1, calculate the number of exploding fireworks; the calculation of the number of exploding fireworks is expressed using a fitness function based on an adaptive factor as follows: in, This indicates that the previous generation of fireworks produced offspring fireworks. The x-coordinate; This indicates that the previous generation of fireworks produced offspring fireworks. The ordinate; This indicates that the previous generation of fireworks was produced. The x-coordinate; This indicates that the previous generation of fireworks was produced. The ordinate; Indicates individual fireworks The fitness function value; Indicates the type of fireworks. This represents the initial minimum value of the fireworks population; This represents the initial maximum value of the fireworks population, calculated as follows: Based on the above fitness function values, we obtain the first... The number of exploding fireworks produced by each firework as follows: In the formula, Indicates the first Fireworks location; The total number of fireworks; This indicates the maximum adaptability value of the fireworks; =5 represents a constant. Indicates individual fireworks The fitness function value; Step 3: Calculate the explosion range of fireworks based on the Piecewise chaos method; Step 4: Perform free variation operation on the position of the fireworks; Step 5: Use a hybrid selection strategy that combines elite selection and roulette wheel selection to select the optimal individual position; Step Six: Save the optimal individual position obtained in Step Five. Finally, the optimal path is formed.

2. The method for optimizing evacuation routes during emergencies based on an optimization algorithm according to claim 1, characterized in that, Step one includes the following sub-steps: Sub-step 1.1: Obtain a two-dimensional planar map of the interior layout, divide it into grids, and generate a gridded evacuation environment; Sub-step 1.2: Introduce the Cubic chaotic mapping into the fireworks initialization: in The first Cubic chaos is represented by the... A sequence; The first Cubic chaos is represented by the... A sequence; Indicates the number of sequences; Represents the chaos coefficient; Sub-step 1.3, Fireworks Population Initialization: Based on the sequence generated by the Cubic chaotic mapping in sub-step 1.2, transform it into the search space of the fireworks population to generate the initial fireworks population: in, Indicates the first Initialize a fireworks population; This represents the initial minimum value of the fireworks population; This indicates the maximum value of the initial fireworks population.

3. The method for optimizing evacuation routes during emergencies based on an optimization algorithm according to claim 2, characterized in that, Step three, which involves calculating the fireworks explosion range using the Piecewise chaos method, is expressed as a piecewise function: in, This represents the explosion amplitude of the i-th firework. In the formula, Indicates the location of the maximum explosion; This indicates the minimum fitness value for fireworks; Indicates the radius of the fireworks blast; exist The value within the function is a piecewise control factor used to divide the piecewise function into four parts. Based on the above-mentioned number of fireworks and explosion range, the position update formula for the i-th firework in the z-direction is as follows: In the formula, This represents the position value of the i-th firework in the z-direction. This represents the Gaussian variation value of the fireworks at the k-th dimension. The selected dimension; For interval A uniformly random number.

4. The method for optimizing evacuation routes during emergencies based on an optimization algorithm according to claim 3, characterized in that, The strategy for freely varying the position of the fireworks described in step four is as follows: in, This indicates the position of the fireworks with the optimal adaptive value, also known as the optimal position. This indicates the current optimal firework is at the [number]th position. Position in dimensions; This indicates that the worst fireworks display is currently in the [number] category. Position in dimensions; Indicates the interval Random numbers that are uniformly distributed.

5. The method for optimizing evacuation routes during emergencies based on an optimization algorithm according to claim 4, characterized in that, The probability of an individual being selected under the hybrid selection strategy described in step five is expressed as follows: in, Indicates individual fireworks The probability of being selected. This represents a constant used to adjust probability values. This indicates the minimum fitness value for fireworks; It is a very small parameter that prevents division by zero.

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