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Genetic algorithm parameter valuing method for weapon-target allocation

A genetic algorithm and target assignment technology, applied in the field of computer simulation and method optimization, can solve the problems that are not conducive to the rapid convergence of the algorithm, easy to destroy the excellent individuals of the population, etc., to accelerate the evolution and convergence, and speed up the speed and high Effects of Crossover and Mutation Probabilities

Pending Publication Date: 2020-09-25
中国人民解放军火箭军工程大学
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Problems solved by technology

In the later stage of optimization, the population individuals are concentrated, and the high crossover probability and mutation probability are easy to destroy the excellent individuals of the population, which is not conducive to the rapid convergence of the algorithm.

Method used

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  • Genetic algorithm parameter valuing method for weapon-target allocation
  • Genetic algorithm parameter valuing method for weapon-target allocation
  • Genetic algorithm parameter valuing method for weapon-target allocation

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Embodiment Construction

[0022] A genetic algorithm parameter value method for weapon-target allocation of the present invention will be further described in detail in conjunction with specific implementation examples below.

[0023] The genetic algorithm parameter selection method of the present invention is applied to the typical air defense weapon-target allocation problem, and four different cases are set based on the same background, and then are solved separately, and the weapon consumption value optimized by each method and the time spent on optimization are compared. A total of five different types of weapons are used to attack a certain number of six types of ground targets, and the average damage coefficient of each type of targets is required to reach 0.8, 0.8, 0.9, 0.85, 0.8, and 0.9 in turn.

[0024]

[0025] Table 1

[0026]

[0027] Table 2

[0028]

[0029] table 3

[0030] Refer to the four different cases set up in Table 1, Table 2 and Table 3, use Matlab software, and use...

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Abstract

The invention discloses a genetic algorithm parameter valuing method for weapon-target allocation, and relates to the field of computer simulation and method optimization. The method comprises the steps of setting self-adaptive crossover probability and mutation probability according to the fitness value of the variant, and respectively establishing continuous functions of the crossover probability and the mutation probability by taking the fitness of the variant as a definition domain; setting a nonlinear function curve, wherein the slope of the function is maximum when the minimum fitness value, the average fitness value and the maximum fitness value are taken. The method has the characteristics that the genetic algorithm can accelerate the speed of jumping out of local optimum and global convergence, and excellent individuals are reserved as much as possible.

Description

technical field [0001] The invention belongs to the field of computer simulation and method optimization, and relates to a genetic algorithm parameter value method for weapon-target allocation. Background technique [0002] With the development of science and technology and intelligent warfare, using computer algorithms to solve weapon-target allocation is an important military problem that all military powers are competing to study. At present, the optimization algorithms used to solve the problem of weapon-target assignment mainly include genetic algorithm, particle swarm algorithm and bat algorithm. Among them, the genetic algorithm has achieved a relatively good optimization effect, but because the crossover and mutation probabilities of the algorithm are always fixed, the convergence speed of the algorithm is slow, and if it falls into a local optimum, it will take a long time to jump out. Crossover and mutation probabilities are key parameters that affect the optimiza...

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Application Information

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IPC IPC(8): G06F17/18G06N3/00G06N3/12
CPCG06F17/18G06N3/006G06N3/126
Inventor 付光远王超魏振华李海龙张卓
Owner 中国人民解放军火箭军工程大学
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