Mass weapon target assignment method based on multiple-target clonal evolutionary algorithm

An evolutionary algorithm and target allocation technology, applied in instruments, calculations, electrical digital data processing, etc., can solve problems such as single-objective optimization, inapplicable weapon-target allocation problems, etc., and achieve good convergence effect

Inactive Publication Date: 2017-04-26
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

The invention patent "A Method for Solving the Problem of Weapon-Target Allocation Based on Differential Evolution Algorithm" (authorized announcement number CN 103336885B) adopts a weapon-target allocation model that not only considers the damage effectiveness of weapons but also the priority of resources, but the model Combining the damage effectiveness of weapons and the priority of resources into an objective function through a weighted method, which is still a single-objective optimization problem
Although these papers and invention patent applications can solve the problem of multi-target weapon-target allocation, their algorithm models are all designed based on small-scale weapons and the number of targets (the number of weapons is less than 50). (the number of weapons exceeds 50), these methods cannot converge to obtain a complete Pareto optimal solution, and cannot be applied to large-scale weapon-target allocation problems

Method used

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  • Mass weapon target assignment method based on multiple-target clonal evolutionary algorithm
  • Mass weapon target assignment method based on multiple-target clonal evolutionary algorithm
  • Mass weapon target assignment method based on multiple-target clonal evolutionary algorithm

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0095] Example 1: The number of weapons is 8; the number of targets is 4; there are 3 weapon platforms; the number of weapons on each platform Pnum=[3 3 2]; the target threat Th=[0.15 0.36 0.18 0.31]; the population size of the subpopulation is 20; the algorithm is the largest The number of iterations is 50; the damage probability D of the weapon platform to the target is:

[0096] D = [0.78 0.76 0.62 0.71;

[0097] 0.92 0.68 0.59 0.59;

[0098] 0.86 0.93 0.77 0.69]

[0099] In order to facilitate the comparison of the convergence of the algorithm, the exhaustive method is used to find the feasible solution and Pareto optimal solution in this example, and the Pareto optimal solution is shown in Table 1.

[0100] Table 1 pareto optimal solution

[0101] g 1 2 3 4 f 0.3348 0.5549 0.6935 0.8315 g 5 6 7 8 f 0.8953 0.9210 0.9389 0.9554

[0102] Independently run the algorithm proposed by the present invention 30 times, and record the fin...

example 2

[0109] Example 2: The number of weapons is 50; the number of targets is 20; there are 10 weapon platforms; the number of weapons on each platform Pnum=[5 5 55 5 5 5 5 5 5]; the target threat Th=[0.02 0.03 0.05 0.08 0.07 0.01 0.09 0.04 0.060 .05 0.05 0.05 0.03 0.07 0.02 0.08 0.04 0.06 0.01 0.09]; the population size of the subpopulation is 20; the maximum number of iterations of the algorithm is 50; the damage probability D of the weapon platform to the target is:

[0110] D=[0.783 0.762 0.627 0.712 0.651 0.794 0.944 0.852 0.969 0.752 0.7930.851 0.896 0.932 0.686 0.914 0.887 0.751 0.692 0.962;

[0111] 0.925 0.683 0.591 0.593 0.892 0.756 0.688 0.763 0.788 0.953 0.9680.863 0.648 0.794 0.814 0.925 0.694 0.832 0.674 0.845;

[0112] 0.866 0.934 0.772 0.695 0.598 0.897 0.754 0.685 0.763 0.755 0.7910.857 0.898 0.936 0.688 0.911 0.888 0.754 0.696 0.976;

[0113]0.927 0.685 0.599 0.591 0.894 0.598 0.897 0.751 0.686 0.768 0.7920.854 0.893 0.938 0.684 0.927 0.696 0.835 0.673 0.847;

[...

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Abstract

The invention provides a mass weapon target assignment method based on a multiple-target clonal evolutionary algorithm, and belongs to the technical field of computer simulation and method optimization. The method comprises the following steps of: firstly, according to the amount of weapons, a plurality of initial sub-populations are generated, the pareto optimal solutions of all sub-populations are calculated, and a dominant population is formed by the optimal solutions; secondly, an algorithm clones all individuals in the dominant population by a cloning mechanism to form a plurality of new sub-populations; and finally, the algorithm gives three special evolution operators and carry out evolution on the individuals by the three evolution operators. By use of the method, a corresponding evolution operator and a corresponding evolution method are designed by aiming at a mass weapon target assignment problem, the mass weapon target assignment problem can be effectively solved, the integral pareto optimal solution can be obtained under the environment of mass weapons and targets, and the method has a good convergence effect.

Description

technical field [0001] The invention belongs to the technical field of computer simulation and method optimization, and relates to a multi-target large-scale weapon-target allocation method, which can be used to calculate the weapon-to-target ratio under multiple optimized targets in an environment with a large number of weapons and a large number of enemy targets. resource allocation plan. Background technique [0002] The Weapon Target Assignment (WTA) problem, also known as firepower assignment, is a process of assigning the optimal attack target to the weapon according to the current environment, combined with the number and characteristics of our weapons, so as to achieve the desired maximum effectiveness. Through the weapon-target allocation, it can provide a reasonable weapon attack plan for the commander, and assist the commander to make a decision. A typical application of the weapon-target allocation method is the anti-drone system in areas such as civil aviation ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 周德云李枭扬潘潜张坤黄吉传吕晓峰
Owner NORTHWESTERN POLYTECHNICAL UNIV
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