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Multi-target weapon-target distribution method based on artificial fish swarm algorithm

An artificial fish swarm algorithm and target allocation technology, applied in constraint-based CAD, instruments, calculations, etc., to achieve fast firepower allocation decisions, small parameter dependence, and reduce external parameter dependence

Inactive Publication Date: 2020-02-07
深圳市白麓嵩天科技有限责任公司
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Problems solved by technology

[0004] The purpose of the present invention is to propose a method for obtaining the Pareto front more accurately in multi-objective WTA, which can avoid the larger problem of deviating from the true Pareto front

Method used

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  • Multi-target weapon-target distribution method based on artificial fish swarm algorithm
  • Multi-target weapon-target distribution method based on artificial fish swarm algorithm
  • Multi-target weapon-target distribution method based on artificial fish swarm algorithm

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Embodiment Construction

[0023] 1. Multi-objective optimization model

[0024] WTA multi-objective optimization is to reduce the cost of ammunition as much as possible when the damage efficiency reaches a certain value. Suppose there are M weapons and N targets, one target can be attacked by multiple weapons at the same time, the optimized mathematical model is as follows.

[0025]

[0026]

[0027]

[0028] In the formula, ω j is the threat level of target (j=1,2,L N)j, p ij Indicates the damage probability of weapon i (i=1, 2, L M) to target j (j = 1, 2, L N), [x ij ] M×N (x ij ={0,1}) is a decision matrix, indicating whether weapon i is assigned to target j, if weapon i is assigned to strike target j, x ij = 1; otherwise, x ij =0. c i Indicates the consumption cost of weapon i when used.

[0029] 2. Artificial fish encoding and decoding

[0030] Since the artificial fish swarm algorithm studies the numerical optimization problem with continuous variables, it cannot be directly us...

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Abstract

The invention discloses a multi-target weapon-target distribution method based on an artificial fish swarm algorithm, aiming to overcome the defect that solving for a WTA problem in the prior art deviates from a real Pareto leading edge greatly. The multi-target weapon-target distribution method comprises the following steps: firstly, randomly generating an initial population, calculating a non-dominated solution set of the initial population, and sorting according to a congestion distance to obtain a globally optimal solution of the initial population; then, according to the clustering behavior of the artificial fish swarm algorithm, enabling other individual fishes in the fish swarm to approach the optimal solution to obtain a new population and a previous non-dominated solution set, andcalculating a new non-dominated solution set; and finally, carrying out crossover variation on the clustered population to increase population diversity, combining with the previous non-dominated solution set again, and carrying out multiple iterations to obtain a final Pareto leading edge. The multi-target weapon-target distribution method is mainly used in the field of fire fighting decision making, is closer to the real Pareto frontier compared with the prior art, has small dependence on parameters, and has great application value in multi-target weapon-target distribution.

Description

technical field [0001] The invention belongs to the field of firepower operation decision-making, in particular to a multi-target weapon based on artificial fish swarm algorithm-a target allocation method. Background technique [0002] Weapon-Target Assignment (WTA for short) is an important issue in the use of firepower. The core issue of WTA is how to assign weapons with different lethality and economic value to different targets to form an overall optimized fire strike system. [0003] Some current methods are focused on the single-objective programming scheme, which is generally based on the objective function optimized for the maximum combat effectiveness, that is, the maximum damage efficiency to enemy targets, and then linear programming methods, genetic algorithms, ant colony algorithms, and tabu search are used. Algorithms, particle swarm optimization methods and other intelligent algorithms for optimization and solution. However, in the actual combat environment,...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06N3/12G06F30/20G06F111/04
CPCG06N3/006G06N3/126
Inventor 叶方邵诗佳孙骞汤春瑞白萍郭小晨张慧宋也
Owner 深圳市白麓嵩天科技有限责任公司
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