CN113554215B - Helicopter scheduling route planning method based on ant colony-genetic fusion algorithm - Google Patents
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Abstract
The invention discloses a helicopter dispatching route planning method based on an ant colony-genetic fusion algorithm, which adopts an ant colony iterative algorithm and a dynamic genetic algorithm to plan a dispatching route in a serial fusion mode, can improve the searching speed, can obtain an optimal route faster when planning a multi-area dispatching route, and is suitable for planning a more complex dispatching route. The first-order ant colony iterative algorithm optimizes the traditional ant colony algorithm by adopting the algorithm of a maximum-minimum ant system and an elite ant system, and improves the searching efficiency of the algorithm. The second-order dynamic genetic algorithm combines the solution set obtained by the first-order algorithm, increases the starting point and the ending point of each region, and refines the starting point and the ending point of the operation region on the basis of the shortest route, so that the scheduling path between the shortest regions is obtained. The invention can shorten the dispatching voyage. The pilot can save working time, improve working efficiency, reduce energy consumption and save cost. The method is simple and practical and has good adaptability.
Description
Technical Field
The invention relates to the field of agriculture, forestry and aviation, in particular to a helicopter dispatching route planning method based on an ant colony-genetic fusion algorithm.
Background
The terrain of the agriculture and forestry area in China is very complex, and the agriculture and forestry area has concentrated large-area areas and mutually independent small-area areas. In the aviation operation process, the operation area is often a plurality of scattered areas, so that the scheduling route planning of a plurality of areas is particularly important. At present, the research on the multi-area dispatching route of aviation operation at home and abroad is less, the requirement of the multi-area plant protection operation of agriculture, forestry and aviation in China is difficult to meet, the yield of agriculture, forestry and crops is influenced, and the development of agricultural economy in China is restricted.
Disclosure of Invention
The invention aims to provide a helicopter scheduling route planning method based on an ant colony-genetic fusion algorithm, which is simple to operate and improves efficiency, aiming at the defects of the prior art. The method can realize shortest dispatching route when the helicopter works in multiple areas, thereby saving the working time and improving the pesticide application efficiency.
In order to achieve the above object, the technical scheme of the present invention is as follows:
a helicopter scheduling route planning method based on an ant colony-genetic fusion algorithm comprises the following steps:
s1, making inscribed circles of all the working areas on a two-dimensional map according to the topographic features of all the working areas, and determining longitude and latitude information of circle centers of the inscribed circles;
s2, coding the circle centers of inscribed circles of all the operation areas in an integer coding mode, and determining the number of the operation areas;
s3, using the circle center of the inscribed circle to represent the operation area where the circle center of the inscribed circle is located, and drawing a dispatching route space diagram according to the coordinate information of the circle center of the inscribed circle and the relative positions of the circle centers of the inscribed circle;
s4, initializing each parameter of the ant colony iterative algorithm, namely, the number of areas c, the number m of ants, the information heuristic factor alpha, the expected heuristic factor beta, the information volatilization coefficient rho, the maximum circulation number Ncmax, the time t, the circulation number Nc, the initialized information quantity tau ij (t) =0 of each route (i, j), the initial time delta tau (0) =0 and the first tabu table allowed k ＝{c-tabu k -a set of application areas that the ant is allowed to access next;
s5, setting a dispatching route space diagram for each ant, and randomly placing m ants on the circle center of each space diagram operation area respectively;
s6, setting one ant number as k, namely the kth ant, and setting an initial value k=1;
s7, selecting a next area by the kth ant according to the selection probability;
s8, updating a second tabu table tabu of the ants k The area which is just accessed by the kth ant is added into a second tabu table, and in order to prevent the ants from selecting the area which is accessed, the second tabu table tabu is adopted k Recording the area where ant k currently walks;
s9, updating the pheromone on the route which is just accessed by the kth ant through the pheromone updating rule;
s10, judging whether k is larger than the number m of ants, if so, indicating that all ants traverse all areas, and adding 1 to the cycle number, namely Nc≡Nc+1, and executing the S11; otherwise, add 1 to the number of ants, i.e. k, step S7, jumping to the step;
s11, judging whether the cycle number Nc reaches the maximum cycle number Ncmax, if so, ending the cycle and outputting an operation result to obtain a scheduling route solution set Gather1; otherwise, updating the upper pheromone of each scheduling route aviation: updating the upper pheromone of each dispatching route aviation by accumulating the pheromones of each route on other maps, and clearing a second tabu table after updating, and jumping to the S5 step;
s12, taking the first-order ant colony iterative algorithm output route solution set Gather1 as an initial scheduling route solution set Gather2 of a second-order dynamic genetic algorithm;
s13, setting the population size, iteration times, variation probability, minimum evolution rate continuous iteration times and second order algorithm maximum iteration times GENMAX2 of a second order dynamic genetic algorithm, and setting the initial value of the iteration times to 1, namely gen2 = 1;
s14, initializing a route solution set Gather2 by adopting a binary coding mode;
s15, calculating the fitness value of a second-order algorithm by combining the operation starting point and the operation end point of each operation area;
s16, sequencing all the routes in the route solution set Gather2 according to the fitness, dividing the numerical value set Gather2 into an upper part and a lower part which are equal by taking the median as a node, and selecting the part of the routes with larger fitness in the route solution set Gather2 for next operation;
s17, calculating the dynamic cross probability of the selected route, performing cross operation and mutation operation according to the dynamic cross probability to obtain a new route set Gather3, endowing the data of the new route set Gather3 to a solution set Gather2, and adding 1 to the iteration times, namely (gen 2) and (gen 2) +1;
s18, checking whether the iteration number (gen 2) reaches the maximum value GENMAX2, outputting a scheduling route with the shortest route in the route solution set Gather2 after the iteration number reaches the maximum value GENMAX2, wherein the route is the optimal route for planning, namely, the optimal scheduling scheme for multi-region route scheduling planning, otherwise, executing S19;
and S19, judging whether the evolution rate of the new route solution set iteration is continuously smaller than the preset minimum evolution rate for a plurality of times, if so, outputting the scheduling route with the shortest route in the new route solution set as the optimal route, otherwise, executing the step S15.
As a further improvement of the above technical solution, the selection probability formula is shown in formula (1):
each ant will search for progress according to equation (1) while the algorithm is running;
wherein τ ij (t) is a pheromone on side (i, j); η (eta) ij The heuristic factor is transferred from the region i to the region j, and the expression is shown in (2);
allowed k ＝{c-tabu k application of ant k to be accessed nextA collection of drug regions; m-total number of ants; d, d ij (i, j=0, 1, ··, n-1) is the distance between the application area i and the application area j, the calculation is shown as a formula (3);
in (x) i ,y i ) And (x) j ,y j ) Coordinates of the application area i and the application area j, respectively; alpha is an information heuristic factor, represents the relative importance degree of the route in an algorithm, reflects the influence degree of the information quantity on the route on the selected route of ants, and has stronger collaboration among ants as the value is larger; beta is a desired heuristic factor that represents the relative importance of visibility.
As a further improvement to the above technical solution, the pheromone update rule includes a state transition rule.
As a further improvement to the above technical solution, the state transition rule adopts a random proportion rule as shown in formula (4):
wherein q is a random variable uniformly distributed in the interval [0,1 ]; q is a preset priori parameter, and the relative importance between the priori knowledge and the new path exploration is determined, wherein Q is more than or equal to 0 and less than or equal to 1.
As a further improvement to the above-described solution,
the pheromone updating rule adopts the pheromone of the ant in the optimal path after each cycle, as shown in formulas (5), (6) and (7):
e-administration route T bs The weight of (2); s-a random variable selected according to the probability distribution given by equation (1).
As a further improvement of the above technical solution, the pheromone updating rule uses the length information of the shortest path constructed by the genetic algorithm, as shown in formula (8):
τ ij (t+n)＝(1-ρ)τ ij +ρτ 0 (8)
τ 0 -length of shortest path constructed by genetic algorithm τ 0 ＝1/D nn ；a>1 is interval coefficient, the control interval stretches and contracts, a is less than or equal to 1/ρ, and tau is present at all times t ij ∈[τ min ,τ max ]And τ ij (0)＝τ max 。
As a further improvement to the above technical solution, the pheromone is limited to a certain range, as shown in formula (9):
as a further improvement to the above technical solution, the maximum pheromone value is set each time the pheromone is initialized; the maximum and minimum pheromone settings are shown in formulas (10) and (11):
τ max ＝a(D nn ) -1 (10)
τ min ＝(aD nn ) -1 (11)
D nn adopting the value as a reference of a maximum and minimum pheromone for an optimal path obtained by using a genetic algorithm; a, a>1 is interval coefficient, the control interval stretches and contracts, a is less than or equal to 1/ρ, and tau is present at all times t ij ∈[τ min ,τ max ]And τ ij (0)＝τ max 。
As a further improvement to the technical scheme, the processing of the airplane take-off and landing points is a key point, the airplane take-off and landing points are regarded as 2 points, but the seats are calibrated to be the same value, so that the fitness value is conveniently calculated, and the fitness value calculating mode is as follows:
taking four areas as examples, the operation sequence is assumed to be ABCD, the operation starting points of the B area can be B1 and B2 or B2 and B1 respectively, and the two states are respectively represented by 0 and 1, then the state code of A1A2B1B2C1C2D1D2 is 0000, that is, the operation sequence is started from A1, enters the a area, and starts from A2, starts from B1, starts from B2, starts from C2, starts from D1, starts from D2, and returns to A1; if the operation sequence is A1A2B2B1C1C2D2D1, the state code is 0101;
fitness f n Designed as the reciprocal of the shortest distance of the scheduling routes among all areas obtained by a second-order algorithm under each entering and exiting state, taking the state code 0101 as an example, if n scheduling routes among areas are obtained by the second-order algorithm, the fitness calculation formula is that
As a further improvement to the above technical solution, in the step S13, the population size in the solution set of the dynamic genetic algorithm is set to be 4 times of the sum of the number of operation areas and the landing points of the aircraft, and the iteration number, the variation probability, the minimum evolution rate, and the continuous iteration number of the minimum evolution rate are set as constants.
Compared with the prior art, the invention has the advantages and positive effects that:
the invention adopts the algorithm design ideas of the maximum-minimum ant system and the elite ant system to optimize the traditional ant colony algorithm and improve the performance of the algorithm. The first-order algorithm can accelerate the initial pheromone accumulation speed, improve the initial searching speed of the algorithm and improve the searching efficiency of the algorithm. Compared with the traditional ant colony algorithm, the method has the advantages that the searching time is greatly shortened, and the planned route range is shorter. The second-order algorithm adopts a fusion mode of a dynamic genetic algorithm, adopts binary coding, combines an initial solution set obtained by the first-order algorithm, increases the starting point and the ending point of each region, re-plans, refines the starting point and the ending point of an operation region on the basis of the shortest route, and thus obtains the scheduling path between the shortest regions. In the aspect of algorithm termination, the invention increases the mode of determining when to stop running the output result by comparing the evolution rate on the basis of setting the evolution algebra of the traditional algorithm. The invention improves the searching speed of the algorithm, and can realize the rapid planning of the operation sequence of the areas when the operation is carried out on a plurality of areas, thereby obtaining the scheduling route with the shortest range; the pilot only needs to fly and apply the medicine according to the planned route, the dispatching route of multi-area operation can be shortened, the working time is saved, the operation efficiency is improved, the use amount of aviation fuel is effectively reduced, and the cost is saved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of an algorithm in the present invention;
FIG. 2 is a diagram of a multi-forest area route planning task;
FIG. 3 is a schematic illustration of a single intra-sector route plan.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, modifications, equivalents, improvements, etc., which are apparent to those skilled in the art without the benefit of this disclosure, are intended to be included within the scope of this invention.
As shown in figures 1, 2 and 3,
assuming that the working area includes 5 discrete areas, the helicopter traverses each area through the entry and exit points of each area. The whole coverage route planning algorithm shown in fig. 3 is adopted to traverse all areas of the area. Each zone must have an entry point and an exit point. The scheduling must be done in and out from two points, but not specifically what point. Such as from A1 in fig. 2, must exit from A2, or from A2, must exit from A1.
The algorithm implementation process is as shown in fig. 1:
1. preparation before application:
(1) And according to the terrain characteristics of each working area, making an inscribed circle of each area on the two-dimensional map, and determining longitude and latitude information of the center of the inscribed circle.
(2) And performing decimal coding on circle centers of inscribed circles of all the areas by adopting an integer coding mode, and determining the number of the areas.
(3) And the circle center is used for representing the area, and a dispatching route air map is drawn according to the coordinate information of the circle center and the relative position of the circle center.
2. Obtaining an optimal scheduling route:
(1) Initializing each parameter of an ant colony iterative algorithm, the number of areas c, the number of ants m, an information heuristic factor alpha, an expected heuristic factor beta, an information volatility coefficient rho, a maximum circulation number Ncmax, time t, circulation number Nc, the initialized information quantity tau ij (t) =0 of each route (i, j), the initial time delta tau (0) =0 and a first tabu table allowed according to a scheduling route air map k ＝{c-tabu k The set of application areas that ant k is allowed to access next, etc.
(2) In the system, a dispatching route space diagram is set for each ant, and m ants are randomly placed on the circle center of the operation area of each m dispatching route space diagrams respectively.
(3) The ant number k, i.e., the kth ant, is set with an initial value k=1.
(4) The kth ant selects the next region according to the selection probability formula.
(5) Updating the second taboo table tabu of the ant k The area which is just accessed by the kth ant is added into a second tabu table, and in order to prevent the ants from selecting the area which is accessed, the second tabu table tabu is adopted k To record the area currently traversed by ant k.
(6) And updating the pheromone on the route which is just accessed by the kth ant through the pheromone updating rule.
(7) Judging whether k is greater than the number m of ants, if so, indicating that all ants traverse all areas, and adding 1 to the cycle number, namely Nc+.Nc+1, and executing the step (8); otherwise, add 1 to the number of ants, i.e. k, and (4) jumping to the step (4).
(8) Judging whether the cycle number Nc reaches the maximum cycle number Ncmax, if so, finishing the cycle and outputting an operation result to obtain a dispatching route solution set Gather1; otherwise, updating the upper pheromone of each scheduling route air map: and (3) updating the upper pheromone of each scheduling route air map by accumulating the pheromone of each route on other maps, and clearing the second tabu after updating, and jumping to the step (2).
(9) And taking the first-order ant colony iterative algorithm output route solution set Gather1 as an initial dispatching route solution set Gather2 of a second-order dynamic genetic algorithm.
(10) Parameters such as the number of navigation lines M2, the variation probability Pm2, the minimum evolution rate U2, the iteration number GENMAX2, the minimum evolution rate continuous iteration number a, the initial value of the iteration number (gen 2) and the like (the parameters are all constants, such as m2=1000, pm2=0.2, u2=1.5%, GENMAX 2=100, a=3, (gen 2) =1) of the second-order dynamic genetic algorithm navigation line solution set are set.
(11) And initializing the route solution set Gather2 by adopting a binary coding mode. The processing of the take-off and landing points of the aircraft is a key point, the take-off and landing points of the aircraft are regarded as 2 points, and the seats are calibrated to be the same value. For example, the operation sequence is ABCDE, the operation start point of the B region may be B1 and B2 or B2 and B1, respectively, and the two states are represented by 0 and 1, respectively, and then the state code of A1A2B1B2C 1D2E1E2 is 00000, which indicates that the operation sequence starts from A1, enters the a region, exits from A2, B1 in, B2 out, C1 in, C2 out, D1 in, D2 out, E1 in, E2 out, and returns to A1; if the job order is A1A2B 1C2D 1E2, the status code is 01010.
(12) Calculating the fitness value f of the second-order algorithm by combining the operation starting point and the operation end point of each operation area n ′；
(13) Sequencing all the routes in the route solution set Gather2 according to the fitness, dividing the numerical set Gather2 into an upper part and a lower part which are equal by taking a median as a node, and selecting the part of the routes with larger fitness in the route solution set Gather2;
(14) By dynamic cross probability P c And (5) calculating the cross probability of each airline according to the formula. And (3) performing cross operation on the selected route, so that the route with high fitness adopts lower cross probability to ensure that the gene with excellent performance is reserved to the next generation. For routes with low fitness, a higher crossover probability is used in order to remove the bad "genes". Obtaining a new route set Gather4, and giving the data of the new route set Gather4 to a solution set Gather2.
(15) The newly obtained route after crossing is subjected to mutation operation, and the iteration number is added by 1, namely (gen 2) + (gen 2) +1. The lost gene is complemented by mutation operation, and meanwhile, the premature convergence problem of the algorithm is prevented.
(16) Checking whether the iteration number reaches 100, and outputting a scheduling route with the shortest route in the new route solution set as an optimal route after the iteration number reaches 100; if the maximum iteration times are not reached, further judging whether the evolution rate of the new route solution set iteration is smaller than the preset minimum evolution rate by 1.5 percent for 3 times continuously, if so, judging that the dynamic genetic algorithm is in a low-speed optimization stage, terminating the genetic algorithm, and outputting the scheduling route with the shortest route in the solution set. Otherwise, executing step (12).
According to the scheduling route planning method, the scheduling route with the shortest route can be planned according to the position distribution of multiple areas, and a pilot flies according to the planned route, so that the operation time can be saved, the operation efficiency can be improved, the using amount of aviation fuel can be reduced, and the application cost can be saved.
Claims (8)
1. A helicopter scheduling route planning method based on an ant colony-genetic fusion algorithm is characterized by comprising the following steps of: the method comprises the following steps:
s1, making inscribed circles of all the working areas on a two-dimensional map according to the topographic features of all the working areas, and determining longitude and latitude information of circle centers of the inscribed circles;
s2, coding the circle centers of inscribed circles of all the operation areas in an integer coding mode, and determining the number of the operation areas;
s3, using the circle center of the inscribed circle to represent the operation area where the circle center of the inscribed circle is located, and drawing a dispatching route space diagram according to the coordinate information of the circle center of the inscribed circle and the relative positions of the circle centers of the inscribed circle;
s4, initializing each parameter of the ant colony iterative algorithm, namely, the number of areas c, the number m of ants, the information heuristic factor alpha, the expected heuristic factor beta, the information volatilization coefficient rho, the maximum circulation number Ncmax, the time t, the circulation number Nc, the initialized information quantity tau ij (t) =0 of each route (i, j), the initial time delta tau (0) =0 and the first tabu table allowed k ＝{c-tabu k -a set of application areas that the ant is allowed to access next;
s5, setting a dispatching route space diagram for each ant, and randomly placing m ants on the circle center of each space diagram operation area respectively;
s6, setting one ant number as k, namely the kth ant, and setting an initial value k=1;
s7, selecting a next area by the kth ant according to the selection probability;
s8, updating a second tabu table tabu of the ants k The area which is just accessed by the kth ant is added into a second tabu table, and in order to prevent the ants from selecting the area which is accessed, the second tabu table tabu is adopted k Recording the area where ant k currently walks;
s9, updating the pheromone on the route which is just accessed by the kth ant through the pheromone updating rule;
s10, judging whether k is larger than the number m of ants, if so, indicating that all ants traverse all areas, and adding 1 to the cycle number, namely Nc≡Nc+1, and executing the S11; otherwise, add 1 to the number of ants, i.e. k, step S7, jumping to the step;
s11, judging whether the cycle number Nc reaches the maximum cycle number Ncmax, if so, ending the cycle and outputting an operation result to obtain a scheduling route solution set Gather1; otherwise, updating the upper pheromone of each scheduling route aviation: updating the upper pheromone of each dispatching route aviation by accumulating the pheromones of each route on other maps, and clearing a second tabu table after updating, and jumping to the S5 step;
s12, taking the first-order ant colony iterative algorithm output route solution set Gather1 as an initial scheduling route solution set Gather2 of a second-order dynamic genetic algorithm;
s13, setting the population size, iteration times, variation probability, minimum evolution rate continuous iteration times and second order algorithm maximum iteration times GENMAX2 of a second order dynamic genetic algorithm, and setting the initial value of the iteration times to 1, namely gen2 = 1;
s14, initializing a route solution set Gather2 by adopting a binary coding mode;
s15, calculating the fitness value of a second-order algorithm by combining the operation starting point and the operation end point of each operation area;
s16, sequencing all the routes in the route solution set Gather2 according to the fitness, dividing the numerical value set Gather2 into an upper part and a lower part which are equal by taking the median as a node, and selecting the part of the routes with larger fitness in the route solution set Gather2 for next operation;
s17, calculating the dynamic cross probability of the selected route, performing cross operation and mutation operation according to the dynamic cross probability to obtain a new route set Gather3, endowing the data of the new route set Gather3 to a solution set Gather2, and adding 1 to the iteration times, namely (gen 2) and (gen 2) +1;
s18, checking whether the iteration number (gen 2) reaches the maximum value GENMAX2, outputting a scheduling route with the shortest route in the route solution set Gather2 after the iteration number reaches the maximum value GENMAX2, wherein the route is the optimal route for planning, namely, the optimal scheduling scheme for multi-region route scheduling planning, otherwise, executing S19;
s19, judging whether the evolution rate of the new route solution set iteration is continuously smaller than the preset minimum evolution rate for a plurality of times, if so, outputting the scheduling route with the shortest route in the new route solution set as the optimal route, otherwise, executing the step S15;
the selection probability formula is shown as formula (1):
each ant will search for progress according to equation (1) while the algorithm is running;
wherein τ ij (t) is a pheromone on side (i, j); η (eta) ij The heuristic factor is transferred from the region i to the region j, and the expression is shown in (2);
allowed k ＝{c-tabu k -is the set of application areas that ant k is allowed to access next; m-total number of ants; d, d ij (i, j=0, 1, ··, n-1) is the distance between the application area i and the application area j, the calculation is shown as a formula (3);
in (x) i ,y i ) And (x) j ,y j ) Coordinates of the application area i and the application area j, respectively; alpha is an information heuristic factor, represents the relative importance degree of the route in an algorithm, reflects the influence degree of the information quantity on the route on the selected route of ants, and has stronger collaboration among ants as the value is larger; beta is a desired heuristic factor representing visibilityRelative importance;
the pheromone update rules include state transition rules.
2. The helicopter scheduling route planning method based on the ant colony-genetic fusion algorithm as claimed in claim 1, wherein: the state transition rule adopts a random proportion rule as shown in the formula (4):
wherein q is a random variable uniformly distributed in the interval [0,1 ]; q is a preset priori parameter, and the relative importance between the priori knowledge and the new path exploration is determined, wherein Q is more than or equal to 0 and less than or equal to 1.
3. The helicopter scheduling route planning method based on the ant colony-genetic fusion algorithm as claimed in claim 1, wherein: the pheromone updating rule adopts the pheromone of the ant in the optimal path after each cycle, as shown in formulas (5), (6) and (7):
e-administration route T bs The weight of (2); s-a random variable selected according to the probability distribution given by equation (1).
4. The helicopter scheduling route planning method based on the ant colony-genetic fusion algorithm as claimed in claim 1, wherein: the pheromone updating rule uses the length information of the shortest path constructed by the genetic algorithm as shown in the formula (8):
τ ij (t+n)＝(1-ρ)τ ij +ρτ 0 (8)
τ 0 -length of shortest path constructed by genetic algorithm τ 0 ＝1/D nn 。
5. The helicopter scheduling route planning method based on the ant colony-genetic fusion algorithm as claimed in claim 1, wherein: pheromones are limited to a certain range, as shown in formula (9):
6. the helicopter scheduling route planning method based on the ant colony-genetic fusion algorithm as claimed in claim 1, wherein: setting the maximum pheromone value when initializing the pheromone each time; the maximum and minimum pheromone settings are shown in formulas (10) and (11):
τ max ＝a(D nn ) -1 (10)
τ min ＝(aD nn ) -1 (11)
D nn adopting the value as a reference of a maximum and minimum pheromone for an optimal path obtained by using a genetic algorithm; a, a>1 is interval coefficient, the control interval stretches and contracts, a is less than or equal to 1/ρ, and tau is present at all times t ij ∈[τ min ,τ max ]And τ ij (0)＝τ max 。
7. The helicopter scheduling route planning method based on the ant colony-genetic fusion algorithm as claimed in claim 1, wherein: the take-off and landing points of the aircraft are considered as 2 points, but the seats are calibrated to the same value.
8. The helicopter scheduling route planning method based on the ant colony-genetic fusion algorithm as claimed in claim 1, wherein: the population size in the solution set of the dynamic genetic algorithm in the step S13 is set to be 4 times of the sum of the number of the operation areas and the take-off and landing points of the airplane, and the iteration times, the variation probability, the minimum evolution rate and the continuous iteration times of the minimum evolution rate are set as constants.
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