CN113112031A - Unmanned aerial vehicle task allocation method based on simulated annealing algorithm - Google Patents

Unmanned aerial vehicle task allocation method based on simulated annealing algorithm Download PDF

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CN113112031A
CN113112031A CN202110233376.7A CN202110233376A CN113112031A CN 113112031 A CN113112031 A CN 113112031A CN 202110233376 A CN202110233376 A CN 202110233376A CN 113112031 A CN113112031 A CN 113112031A
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吴学礼
尹雅楠
甄然
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Hebei University of Science and Technology
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Abstract

The invention relates to an unmanned aerial vehicle task allocation method based on a simulated annealing algorithm, which changes the condition of receiving a new solution in the Metropolis acceptance criterion of the simulated annealing algorithm, improves the Metropolis acceptance criterion in a threshold setting mode, can make the algorithm jump out of local optimum, and can reduce the random acceptance of the new solution which is much different from the current solution. By improving the algorithm, the running time of the program is greatly shortened.

Description

Unmanned aerial vehicle task allocation method based on simulated annealing algorithm
Technical Field
The invention relates to an unmanned aerial vehicle task allocation method based on a simulated annealing algorithm.
Background
The simulated annealing algorithm is derived from the solid annealing principle, is a probability-based algorithm, and starts from the similarity between the annealing process based on solid substances in physics and a general combinatorial optimization problem. The simulated annealing algorithm starts from a certain high initial temperature, and randomly searches a global optimal solution of the objective function in a solution space by combining with the probability jump characteristic along with the continuous decrease of the temperature parameter, namely when the solution is in a local optimal solution, the local optimal solution can be probabilistically jumped out according to the Metropolis acceptance criterion and finally tends to the global optimal solution. The simulated annealing algorithm is a general optimization algorithm, and theoretically, the algorithm has probabilistic global optimization performance.
At present, in unmanned aerial vehicle task allocation, a simulated annealing algorithm is mostly used as an aid of other algorithms, unmanned aerial vehicle task allocation is not performed only by using the simulated annealing algorithm, when an obtained new solution is larger than a current solution (namely the new solution is worse than the current solution) in a calculation process, whether the solution is accepted or not is judged according to a Metropolis acceptance criterion in simulated annealing, and the reason is that the solution is accepted or not in judgment
Figure BDA0002959548140000011
Is compared with the random number rand, there will be a new solution that is not necessarily the best solution at present, and at the same time the new solution may differ greatly from the current solution, which will increase the time for the algorithm to find the optimal solution.
Disclosure of Invention
The invention aims to solve the technical problem of providing an unmanned aerial vehicle task allocation method based on a simulated annealing algorithm, and reducing the problem that the algorithm takes too much time.
The technical scheme adopted by the invention is as follows:
an unmanned aerial vehicle task allocation method based on a simulated annealing algorithm is characterized by comprising the following steps:
the method comprises the following steps: initializing, setting an initial temperature T, an initial distribution sequence S and the iteration number L of each temperature value T;
step two: performing steps three to six on k-1, …, L;
step three: generating a new allocation order S1；
Step four: calculating the increment Δ C ═ C (S)1) -c(s), wherein c(s) is the current objective function value;
step five: if deltaC is less than 0, the solution obtained by the new distribution sequence is better than the solution obtained by the current distribution sequence, and the new solution is accepted; if Δ C ≧ 0, it means that the solution obtained for the current allocation order is better than the solution obtained for the new allocation order, the new solution is selectively accepted using equation (1),
ymin≤△C≤ymax (1)
y in formula (1)min、ymaxLower and upper thresholds, respectively;
if the Delta C is in the threshold range of the formula (1), a new solution is accepted, otherwise, the original solution is kept;
step six: saving the optimal solution of the iteration, ending the program if the termination condition is met, and outputting the optimal solution;
step seven: and T is gradually reduced, and when T is larger than or equal to 1, the step II is skipped.
The invention has the positive effects that: the invention improves the acceptance criterion of Metropolis in the simulated annealing algorithm into ymin≤△C≤ymax. The threshold range is set, so that the algorithm can jump out of local optimum, the accepting of new solutions which are different from the current solution due to randomness can be reduced, and the running time of the algorithm is greatly shortened by improving the algorithm.
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FIG. 1 is a block diagram of the present invention;
FIG. 2 is a block diagram of an improved Metropolis acceptance criteria framework of the present invention.
Detailed Description
As shown in fig. 1 and 2, the method comprises the following steps:
the method comprises the following steps: initializing, setting an initial temperature T, an initial distribution sequence S and the iteration number L of each temperature value T.
Step two: step three to step six are performed for k 1, …, L.
Step three: generating a new allocation order S1。
Step four: calculating the increment Δ C ═ C (S)1) -c(s), wherein c(s) is the current objective function value.
Step five: if deltaC is less than 0, the new solution is better than the current solution, and the new solution is accepted;
if Δ C is greater than or equal to 0, it indicates that the solution obtained by the new distribution sequence is better than the solution obtained by the current distribution sequence, and it is necessary to accept a new solution worse than the current solution in the operation of the simulated annealing algorithm, because proper acceptance of such a solution can help the algorithm jump out of local optimum, so that a better solution can be found. In order to solve the problem that the traditional simulated annealing algorithm takes too much time when the task allocation of the unmanned aerial vehicle is solved, the method changes the condition of accepting a new solution in the Metropolis acceptance criterion of the simulated annealing algorithm. By selectively accepting the new solution using equation (1),
ymin≤△C≤ymax (1)
y in formula (1)min、ymaxRespectively a lower limit value and an upper limit value of the threshold, finding the minimum difference value between the lower limit value and the upper limit value in the result obtained by each iteration according to the operation result to make the minimum difference value be the upper limit value, and adjusting the lower limit value according to the upper limit value, so that the difference between the accepted poor solution and the current solution is not too much; if the Delta C is in the threshold range of the formula (1), a new solution is accepted, otherwise, the original solution is kept;
the upper and lower limits of the threshold are adjusted after the program has been run so that the algorithm accepts as much as possible a new solution that is not as different from the current solution. When the obtained new solution is worse than the current solution, whether the new solution is accepted or not is considered by judging whether the difference value of the new solution and the current solution is within the set threshold value range or not. The threshold range is set, so that the algorithm can jump out of local optimum, and the acceptance of new solutions which are different from the current solution due to random can be reduced. By improving the algorithm, the running time of the program is greatly shortened.
Step six: and storing the optimal solution of the iteration, ending the program if the termination condition is met, and outputting the optimal solution.
Step seven: and T is gradually reduced, and when T is larger than or equal to 1, the step II is skipped.
S represents the sequence of task allocation, S can be randomly changed in each iteration, different solutions are obtained in different sequences, the best solution is found out through a simulated annealing algorithm, and the allocation sequence corresponding to the solution is the required allocation sequence.
Table 1 below is the task assignment results using the prior art and fig. 2 is the results using the assignment method of the present invention.
Table 1 task assignment results using the prior art
Figure BDA0002959548140000031
Figure BDA0002959548140000041
Figure BDA0002959548140000051
TABLE 2 results of using the distribution method of the invention
Figure BDA0002959548140000052
Figure BDA0002959548140000061
Table 1 and table 2 show that each four rows are an allocation result, the first row is the reconnaissance of the unmanned aerial vehicle 1, the second row is the reconnaissance of the unmanned aerial vehicle 2, the third row is the strike of the unmanned aerial vehicle 1, and the fourth row is the strike of the unmanned aerial vehicle 2. The first column is the scout activity sequence to execute task 1, the second column is the scout activity sequence to execute task 2, the third column is the scout activity sequence to execute task 3, the fourth column is the percussive activity sequence to execute task 1, the fifth column is the percussive activity sequence to execute task 2, and the sixth column is the percussive activity sequence to execute task 3.
The final distribution result is: the unmanned aerial vehicle 1 executes a task 1 (reconnaissance and striking) and then executes a task 2 (striking); the drone 2 performs task 2 (scout) and then task 3 (scout, strike). As can be seen from fig. 1 and 2, the improved algorithm does not affect the result of task allocation. According to the running time calculation of the algorithm, the simulation time of the algorithm in the figure 1 is 173.3739s, the simulation time of the algorithm in the figure 2 is 21.5166s, and the simulation time of the algorithm after improvement is reduced by 87.5895%.
The invention improves the acceptance criterion of MetropolisIs ymin≤△C≤ymaxWhether to accept the new solution is considered by judging whether the difference between the new solution and the current solution is within the set threshold range. The threshold range is set, so that the algorithm can jump out of local optimum, the overlong running time of the algorithm caused by accepting a new solution which is worse than the current solution can be reduced, the distribution result of the improved simulated annealing algorithm is the same as the distribution result of the algorithm before improvement through simulation verification, the objective function value is the same, and the running time of the improved algorithm is reduced by 87.5895% compared with the running time of the algorithm before improvement.

Claims (1)

1. An unmanned aerial vehicle task allocation method based on a simulated annealing algorithm is characterized by comprising the following steps:
the method comprises the following steps: initializing, setting an initial temperature T, an initial distribution sequence S and the iteration number L of each temperature value T;
step two: performing steps three to six on k-1, …, L;
step three: generating a new allocation order S1；
Step four: calculating the increment Δ C ═ C (S)1) -c(s), wherein c(s) is the current objective function value;
step five: if deltaC is less than 0, the solution obtained by the new distribution sequence is better than the solution obtained by the current distribution sequence, and the new solution is accepted; if Δ C ≧ 0, it means that the solution obtained for the current allocation order is better than the solution obtained for the new allocation order, the new solution is selectively accepted using equation (1),
ymin≤△C≤ymax (1)
y in formula (1)min、ymaxRespectively a lower limit value and an upper limit value of the threshold, and finding the minimum difference value of the lower limit value and the upper limit value in the result obtained by each iteration according to the operation result to make the difference value be the upper limit value;
if the Delta C is in the threshold range of the formula (1), a new solution is accepted, otherwise, the original solution is kept;
step six: saving the optimal solution of the iteration, ending the program if the termination condition is met, and outputting the optimal solution;
step seven: and T is gradually reduced, and when T is larger than or equal to 1, the step II is skipped.
CN202110233376.7A 2021-03-03 2021-03-03 Unmanned aerial vehicle task allocation method based on simulated annealing algorithm Pending CN113112031A (en)

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CN113487264B (en) * 2021-07-20 2022-09-02 中南大学 Logistics distribution method and system based on heterogeneous multi-unmanned aerial vehicles

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