Remote guidance rocket cluster firepower distribution method based on wolf pack algorithm

Abstract

The invention discloses a remote guidance rocket cluster firepower distribution method based on a wolf pack algorithm, and the method comprises the steps: considering the effective killing purpose of a remote guidance rocket cluster, saving rocket cluster firepower resources, meeting the requirements of task constraint and the like, building a remote guidance rocket cluster firepower distribution mathematical model, and then introducing the wolf pack algorithm, the method comprises the following steps of: designing migration and calling operators to have intelligent target attacking capability for a remote guidance rocket cluster, and finally, combining an ant colony algorithm, enhancing the cognition of the remote guidance rocket cluster on a detected target by adding pheromone guidance, introducing pheromone inspiration in an updating link of the remote guidance rocket cluster, inheriting'wisdom 'of rocket cluster attacking, and improving the target attacking capability of the remote guidance rocket cluster. And the search efficiency of the wolf pack algorithm is improved. The multi-target firepower distribution method can be used for firepower distribution of a remote guidance rocket cluster aiming at multiple targets.

Description

Technical Field

[0001] The invention belongs to the field of rocket technology, and in particular relates to a firepower distribution method for a long-range guided rocket cluster based on a wolf pack algorithm. Background Art

[0002] Traditional long-range guided rockets generally use the "acupuncture point" method to strike, and after the strike is completed, it is necessary to judge whether a second strike is needed based on the degree of damage. However, with the continuous development of the concept of global combat, in the future battlefield, not only will the command of operations need to consider the issue of "the first battle is the decisive battle", but long-range guided rockets will also need to face the combat environment of "only one salvo". In the process of performing combat missions, it is necessary to consider the firepower distribution method of the long-range guided rocket cluster in a highly confrontational battlefield environment after the salvo, and consider how to give full play to the overall coordination advantages, efficiently and reasonably plan the attack firepower resources, realize the coordinated attack of multiple combat units, and at the same time minimize the consumption of combat units and improve combat effectiveness.

[0003] The problem of firepower allocation has always been one of the core contents in the research of combat command decision support. Algorithms for solving the problem of firepower resource allocation can be divided into traditional algorithms and intelligent optimization algorithms. Traditional algorithms include linear programming, branch and bound method, dynamic programming method, etc. Intelligent optimization algorithms include improved genetic algorithm, ant colony algorithm, adaptive ant colony optimization algorithm and bat algorithm improved by dynamic difference, etc. Wolf Pack Algorithm (WPA) is a group intelligent optimization algorithm, which has been successfully applied in the fields of high-dimensional complex function optimization, knapsack problem, UAV trajectory planning and other optimization problems.

[0004] For the problem of firepower distribution, although traditional algorithms and intelligent optimization algorithms can obtain satisfactory solutions, they have the defects of premature maturity, slow evolution or difficulty in algorithm design and implementation. It is very difficult to find the optimal solution within the actual allowed time, and the only satisfactory solution can be found based on the assumed combat principles. The wolf pack algorithm also has the disadvantages of low algorithm optimization accuracy, easy to fall into local optimality and low efficiency. The above algorithms are difficult to meet the firepower distribution problem of long-range guided rocket clusters. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the present invention provides a method for allocating firepower of a long-range guided rocket cluster based on a wolf pack algorithm, including taking into account the requirements of achieving effective killing purposes of a long-range guided rocket cluster, saving rocket cluster firepower resources and meeting task constraints, establishing a mathematical model for allocating firepower of a long-range guided rocket cluster, and then introducing a wolf pack algorithm, designing wandering and summoning operators, so that the long-range guided rocket cluster has the ability to attack targets intelligently, and finally, combining an ant colony algorithm, by adding pheromone guidance, strengthening the recognition of the long-range guided rocket cluster on the detected target, introducing pheromone inspiration in the update link of the long-range guided rocket cluster, inheriting the "wisdom" of the rocket cluster attack, and improving the search efficiency of the wolf pack algorithm. The invention can be used for the firepower allocation of a long-range guided rocket cluster against multiple targets.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] Step 1: Establish a specific scenario for long-range guided rocket cluster combat to attack multiple targets;

[0008] Step 1-1: Create the following scene:

[0009] (1) There are a total of V-type rockets in the long-range guided rocket cluster, and the number of Z-type rockets is , z=1,2,…v, and , m represents the total number of rockets;

[0010] (2) Each rocket is a combat unit, carrying one type of warhead;

[0011] (3) The total number of targets is n, and the value of the g-th target is ; is the single kill probability of the z-th rocket attacking the g-th target;

[0012] (4) Decision variables is the number of z-th rockets used to attack the g-th target, , the probability of the z-th rocket killing the g-th target for:

[0013] (1)

[0014] (5) The combined kill probability of all V-shaped rockets on the g-th target for:

[0015] (2)

[0016] (6) The mathematical model of firepower distribution is expressed as follows:

[0017] (3)

[0018] in, is the maximum target value benefit, for The mean of

[0019] Step 1-2: The constraints of the firepower allocation mathematical model include:

[0020] (1) Task completion constraints;

[0021] (4)

[0022] Where: The total number of rockets allocated to the g-th target;

[0023] (2) Effective killing constraints;

[0024] (5)

[0025] Where: is the preset killing probability threshold of the g-th target;

[0026] (3) Attack consumption constraints;

[0027] (6)

[0028] Step 1-3: The long-range guided rocket cluster model features include the following aspects:

[0029] (1) Ensure that every target can be effectively killed;

[0030] By measuring whether the comprehensive killing probability of the target exceeds the preset killing probability threshold, it is determined whether the target is effectively killed. If it is lower than the preset killing probability threshold, the allocation to the target is considered invalid.

[0031] (2) Set target value , ensuring the target value High goals are assigned priority;

[0032] (3) Size Compare right The value has a greater impact. The smaller the value, the greater the average value of the comprehensive kill probability;

[0033] (4) Guarantee target When the target of the assigned rocket is the same, that is, Same, select A large rocket combination makes the overall kill probability as high as possible;

[0034] Step 2: Establish the movement rules and intelligent behavior of long-range guided rocket clusters based on the wolf pack algorithm;

[0035] Step 2-1: In the long-range guided rocket cluster firepower allocation problem, an integer-based encoding method is used, as follows:

[0036] (1) Allocation plan ,variable is an integer between 0 and n, Indicates that the Rockets are allocated to goals, Indicates that the j-th rocket is not assigned to any target;

[0037] (2) Use the rocket position to represent a candidate allocation scheme: Assume that the rocket position vector dimension is also , the total number of artificial wolf rockets is ,exist Rocket in European Space Location , For the Rockets in the The position in the dimensional variable space;

[0038] (3) Amount of target information detected by the rocket , that is, the objective function value, the rocket With Rocket Distance Manhattan distance for the position encoding of the two:

[0039] (7)

[0040] Step 2-2: Define the encoding method in step 2-1 as follows:

[0041] (1) Define the walk operator , the position of rocket i is , is the set of coding bits, i.e., the attack range of the artificial wolf rocket; r is the number of coding bits to be changed, i.e., the movement step length of the rocket; the walk operator represents Randomly select r code bits to form a set ,Will Change to a random number selected from the j-dimensional variable space;

[0042] (2) Define the summon operator , the position of rocket i is , Rocket location for artificial wolf and Rocket's position as the leader of the WPA The set of different coding bits, which is not an empty set. The rocket of the alpha wolf in WPA is referred to as the "alpha wolf rocket"; the set for:

[0043] (8)

[0044] In the formula, k represents the number of iterations, the initial value of k is 1; null represents a null value; Indicates the j-th dimension value of the alpha wolf position;

[0045] Summon Counter For Randomly select r code bits to form a set , and The value is changed according to formula (9), which means that rocket i receives part of the information transmitted by the leader rocket:

[0046] (9)

[0047] Step 2-3: Establish behavior rules and intelligent behaviors of long-range guided rocket clusters based on wolf pack algorithm:

[0048] (1) Rules of conduct for the Alpha Wolf Rocket:

[0049] The rocket with the best objective function value is the leader rocket. During the iteration process, the leader rocket is replaced according to the objective function. The leader rocket does not perform wandering, summoning, or siege behaviors.

[0050] (2) Wandering behavior:

[0051] The best rocket is selected as the alpha rocket, and all rockets except the alpha rocket are used as the alpha rockets in WPA, referred to as "alpha rockets". Take a tentative step in h directions, that is, to probe the position of the Wolf rocket Execute h walk operators ; Then the obtained h new positions Make a greedy decision, that is, choose the position with the best objective function value and better than the original position objective function value as the new position of the rocket; let the objective function values ​​corresponding to the positions of the exploratory wolf rocket i and the head wolf rocket be and , the number of walks is T, and the maximum number of walks is Repeat the above process until or ;

[0052] (3) summoning behavior;

[0053] Define that except for the Alpha Wolf rocket, all the rockets in the long-range guided rocket cluster are the rockets of the Wolf in WPA, referred to as "Wolf Rockets". The Wolf Rockets are summoned by the Alpha Wolf with a running step length of Rocket to the location of the wolf leader Closer, that is, the position of the Wolf Rocket Executes a summon operator once ;like Execute the walk operator when it is an empty set ;like ,but , Wolf Rocket i replaces Wolf Rocket for summoning; if , then Wolf Rocket i continues to charge until the distance between Wolf Rocket i and the Alpha Wolf Rocket is less than the judgment distance ;

[0054] (4) Behavior of siege;

[0055] The location of the Wolf's rocket The position of the target, the position of the rockets involved in the siege According to formula (10), the position is transformed to obtain the new position: compare the target information detected by the rocket at the new and old positions before and after the siege and make a greedy decision;

[0056] (10)

[0057] Walking step length , running stride length , Siege Step Length All are integers, indicating the precision of the artificial wolf search; is an intermediate variable;

[0058] (5) Rocket cluster update mechanism;

[0059] According to the rocket cluster update mechanism, the group is updated, that is, eliminated Rockets, is a random integer between [N / (2U), N / U], and U is the update scale factor; the new leader rocket obtains part of the coding position of the previous leader rocket, and the new rocket position Calculated by formula (11):

[0060] (11)

[0061] In the formula, The location of the Alpha Wolf rocket; ; The number of coded bit changes L is calculated by formula (12):

[0062] (12)

[0063] Where: To round down; is the maximum number of iterations of the algorithm; represents the number of encoding bits of the target g changed at the kth iteration;

[0064] Step 3: Establish a target allocation scheme based on ant colony algorithm;

[0065] Step 3-1: Establish a target allocation scheme based on the ant colony algorithm to replace the wandering operator, and improve the firepower allocation and reaction speed of the long-range guided rocket cluster based on the wolf pack algorithm;

[0066] According to the ant colony algorithm, starting from the first rocket, a target is selected from all targets and assigned to the rocket; then for the second rocket, a target is selected from the target set currently assigned and assigned to the rocket; the assignment is carried out in this order to guide the completion of all target assignments; at this time, the specific rules inspired by pheromones in the ant colony algorithm are as follows:

[0067] (1) State transition rules;

[0068] Select target g according to pseudo-random rule and assign it to the u-th rocket;

[0069] (13)

[0070] Where: is the pheromone between the u-th rocket and the target t at the k-th iteration; For heuristic information related to the problem, in the firepower allocation problem, is the product of the probability of the u-th rocket killing target t and the value of target t; and They are and The relative importance of; q is a random number uniformly distributed between 0 and 1; is a specified constant; G is a random proportion rule according to the following Choose a goal from the list;

[0071] (14)

[0072] Where: is the currently allocatable target set; is the product of the probability of the u-th rocket killing target g and the value of target g; is the pheromone between the u-th rocket and the target g at the k-th iteration; represents the random ratio rule for selecting target g for the u-th rocket at the k-th iteration;

[0073] (2) Pheromone update rules;

[0074] After one iteration process is completed, the pheromone is adjusted according to the global update rule of formula (15):

[0075] (15)

[0076] In the formula, Select the pheromone increment corresponding to target g for the u-th rocket; It is the volatile factor of pheromone; is the pheromone between the u-th rocket and the target g at the k+1th iteration;

[0077] (16)

[0078] In the formula, Q is the pheromone intensity, which is a constant; is the optimal value of the objective function for the kth iteration; is the corresponding optimal solution.

[0079] Step 3-2: The specific method of adding pheromone guidance is that when the rocket is wandering and the rocket position executes the wandering operator, the corresponding coding bit is changed from selecting a random number to selecting a new position according to formula (13) and formula (14); when updating the pheromone used in the rocket cluster, when each rocket generates a new position according to formula (12), its coding bit calculates its change value according to formula (13) and formula (14); the timing of pheromone update is selected after the rocket cluster completes the hunt and before updating the cluster state in each iteration, and the pheromone is globally updated according to formula (15) using the position information of the leader rocket and its objective function value.

[0080] Preferably, the pheromone volatile factor The value range is .

[0081] The beneficial effects of the present invention are as follows:

[0082] The present invention solves the problem of firepower allocation in coordinated attacks on multiple targets by long-range guided rocket clusters. The method can realize intelligent firepower allocation to multiple targets when facing multi-task targets, effectively destroying missile combat targets while reducing rocket losses, improving rapid responsiveness in multi-target combat, and systematically improving the performance of long-range guided rocket clusters in optimization accuracy and stability, effectively expanding the ability of the army's long-range rockets to carry out diversified firepower strike missions, and supporting the formation of new tactics for the army's long-range firepower strike. DETAILED DESCRIPTION

[0083] The present invention is further described below in conjunction with embodiments.

[0084] In order to solve the problem of firepower allocation of long-range guided rocket clusters, the present invention proposes a firepower allocation method for long-range guided rocket clusters based on wolf pack algorithm. Starting from the specific scenario of long-range guided rocket cluster combat attacking multiple targets, the method establishes the movement rules and intelligent behavior of long-range guided rocket clusters based on wolf pack algorithm, and improves WPA by introducing pheromone inspiration rules in ant colony algorithm. According to the update rules of artificial wolves in wandering behavior, it is proposed to use pheromones to guide the search process, and the wolf pack's "memory" of previous hunts is formed by continuously updating pheromones, and applied to the wolf pack update mechanism to improve the global search capability of the algorithm.

[0085] The purpose of the present invention is to achieve the following measures:

[0086] Step 1: Establish a specific scenario for long-range guided rocket cluster combat to attack multiple targets;

[0087] When conducting cluster operations, the rockets will be adjusted according to the target situation in order to quickly adapt to the mission requirements, cooperate in combat, and achieve the combat objectives with less consumption. The following scenario is now established:

[0088] (1) A long-range guided rocket cluster consists of a total of V-type rockets, and the number of Z-type rockets is (z=1,2,…v), and ;

[0089] (2) Each rocket is a combat unit, carrying one type of warhead;

[0090] (3) The total number of targets is n, and the value of the g-th target is ; is the single kill probability of the z-th rocket attacking the g-th target;

[0091] (4) Decision variables is the number of z-th rockets used to attack the g-th target, , the probability of the z-th rocket killing the g-th target for:

[0092] (1)

[0093] (5) The combined kill probability of all V-shaped rockets on the g-th target for:

[0094] (2)

[0095] (6) In order to achieve the optimal overall combat effectiveness of the long-range guided rocket cluster combat system, it is necessary to consume fewer rocket combat units while ensuring the completion of the mission, so that the target value benefit F after the attack is maximized. At this time, the mathematical model of firepower allocation can be expressed as follows:

[0096] (3)

[0097] The constraints of the firepower allocation mathematical model include:

[0098] (1) Mission completion constraint: Each target is assigned at least one rocket to attack it.

[0099] (4)

[0100] Where: The total number of rockets allocated to the g-th target.

[0101] (2) Effective kill constraint: This constraint requires that the comprehensive kill probability of each target is greater than the preset kill probability threshold.

[0102] (5)

[0103] Where: is the preset killing probability threshold of the g-th target, which can be specified according to the specific situation.

[0104] (3) Attack consumption constraints. The number of rockets used for attack cannot exceed the limit.

[0105] (6)

[0106] In this scenario, the characteristics of the long-range guided rocket cluster model include the following aspects:

[0107] (1) Ensure that each target can be effectively killed. By measuring whether the comprehensive kill probability of the target exceeds the preset kill probability threshold, it is determined whether the target is effectively killed. If it is lower than the preset kill probability threshold, the allocation of the target is considered invalid.

[0108] (2) Set target value , ensuring the target value High goals are assigned priority;

[0109] (3) Size Compare right The value has a greater impact. The smaller the value, the greater the average value of the combined kill probability, so the model can ensure that fewer rockets are used.

[0110] (4) Guarantee target When the target of the assigned rocket is the same, that is, Same, select A large rocket combination makes the overall kill probability as large as possible.

[0111] Step 2: Establish the movement rules and intelligent behavior of long-range guided rocket clusters based on the wolf pack algorithm;

[0112] The Wolf Pack Algorithm (WPA) is a swarm intelligence optimization algorithm that simulates the division of labor and cooperative hunting behavior of wolves and the rules of prey distribution. It ultimately realizes the entire hunting process of wolves through the detection of prey odor and environmental information by individual wolves, the sharing and interaction of information among artificial wolves, and the individual behavioral decisions of artificial wolves based on their own responsibilities.

[0113] According to the characteristics of wolf packs, WPA adopts a bottom-up design method based on artificial wolf subjects and a collaborative search path structure based on division of responsibilities. It divides artificial wolves into alpha wolves, scout wolves, and fierce wolves. The entire hunting process is abstracted into three intelligent behaviors: wandering, summoning, and siege, as well as the alpha wolf production rule of "the winner is king" and the wolf pack update mechanism of "survival of the strong".

[0114] In the firepower allocation problem of long-range guided rocket clusters, an integer-based encoding method is used, as follows:

[0115] (1) Allocation plan ,variable is an integer between 0 and n, Indicates that the Rockets are allocated to goals, indicates that the jth rocket is not assigned to any target;

[0116] (2) Use the rocket position to represent a candidate allocation scheme. Suppose the rocket position vector dimension is (Total number of rockets), is the total number of artificial wolf rockets, Rocket in European Space Location , For the Rockets in the The position in the variable space.

[0117] (3) Amount of target information detected by the rocket , that is, the objective function value, the rocket With Rocket Distance Manhattan distance for the position encoding of the two:

[0118] (7)

[0119] For the above encoding method, the following definitions are made:

[0120] (1) Define the walk operator , the position of rocket i is , is the set of coding bits, which can be understood as the attack range of the artificial wolf rocket, and r is the number of coding bits to be changed, which can be understood as the movement step length of the rocket. The walk operator is represented in Randomly select r code bits to form a set ,Will Change to a random number selected from the j-dimensional variable space;

[0121] (2) Define the summon operator , the position of rocket i is , Rocket location for artificial wolf and the position of Rocket, the alpha wolf in the WPA (hereinafter referred to as "alpha wolf Rocket") The set of different encoding bits, and not an empty set, the set for:

[0122] (8)

[0123] Summon Counter For Randomly select r code bits to form a set , and The value is changed according to formula (9), which can be understood as rocket i receiving part of the information transmitted by the leader rocket:

[0124] (9)

[0125] Based on the above coding methods and definitions, the behavior rules and intelligent behaviors of the long-range guided rocket cluster based on the wolf pack algorithm are established as follows:

[0126] (1) Behavior rules for the alpha rocket. The rocket with the best objective function value in the algorithm is the alpha rocket. During the iteration process, the alpha rocket is replaced according to the objective function. The alpha rocket does not perform roaming, summoning, or siege behaviors.

[0127] (2) Wandering behavior. The best rocket is selected as the alpha rocket, and all rockets except the alpha rocket are used as the exploratory rockets in WPA (hereinafter referred to as "exploratory rockets"). The exploratory rockets are based on the wandering step length Take a tentative step in h directions, that is, to probe the position of the Wolf rocket Execute h walk operators . Then the obtained h new positions Make a greedy decision, that is, choose the position with the best objective function value and better than the original position objective function value as the new position of the rocket. Suppose the objective function values ​​corresponding to the positions of the exploratory wolf rocket i and the head wolf rocket are and , the number of walks is T, and the maximum number of walks is Repeat the above process until or .

[0128] (3) Summoning behavior. Define all rockets in the long-range guided rocket cluster, except for the alpha wolf rocket, as the rockets of the fierce wolf in WPA (hereinafter referred to as "fierce wolf rocket"). The fierce wolf rocket is summoned by the alpha wolf and moves quickly with a larger attack step length. Rocket to the location of the wolf leader Closer, that is, the position of the Wolf Rocket Executes a summon operator once .like Execute the walk operator when it is an empty set .like ,but , Wolf Rocket i replaces Wolf Rocket for summoning; if , then Wolf Rocket i continues to charge until the distance between Wolf Rocket i and the Alpha Wolf Rocket is less than the judgment distance .

[0129] (4) Behavior of siege. The position of the target, the position of the rockets involved in the siege According to formula (10), the position is transformed to obtain the new position: compare the target information detected at the new and old positions before and after the rocket implements the siege behavior and make a greedy decision.

[0130] (10)

[0131] (5) Rocket Cluster Update Mechanism. The group is updated according to the rocket cluster update mechanism, that is, the Rockets, is a random integer between [N / (2U), N / U], and U is the update scale factor. The new leader rocket obtains part of the coding position of the previous leader rocket, and the new rocket position Calculated by formula (11):

[0132] (11)

[0133] The number of coded bit changes L is calculated by formula (12):

[0134] (12)

[0135] The above established the movement rules and intelligent behaviors of long-range guided rocket clusters based on the wolf pack algorithm.

[0136] Step 3: Establish a target allocation scheme based on ant colony algorithm;

[0137] Since the long-range braking rocket cluster has the characteristics of fast flight speed and short reaction time, it is necessary to update the wandering operator Ω in WPA to improve its distributed positive feedback parallel computing capability to improve the rapid reaction capability within the long-range guided rocket cluster. Now, a target allocation scheme based on ant colony algorithm is established to replace the wandering operator Ω, and improve the firepower allocation and reaction speed of the long-range guided rocket cluster based on wolf pack algorithm.

[0138] According to the ant colony algorithm, starting from the first rocket, a target is selected from all targets and assigned to the rocket; then for the second rocket, a target is selected from the target set currently assigned and assigned to the rocket; and the assignment is carried out in this order to guide the completion of all target assignments. At this time, the specific rules inspired by pheromones in the algorithm are as follows:

[0139] (1) State transition rule: Select target g and assign it to the u-th rocket according to the pseudo-random rule.

[0140] (13)

[0141] Where: For heuristic information related to the problem, in the firepower allocation problem, According to the optimal criterion of the mathematical model, in the present invention, it is the product of the probability of a single kill of the u-th rocket on the target t and the value of the target t;

[0142] (14)

[0143] (2) Pheromone update rule. After an iteration process is completed, according to the current optimal objective function value and its corresponding solution , adjust the pheromone according to the global update rule of formula (15):

[0144] (15)

[0145] (16)

[0146] The specific method of adding pheromone guidance to the firepower allocation method of long-range guided rocket cluster based on wolf pack algorithm established by steps 1 and 2 is that when the rocket is wandering and the rocket position executes the wandering operator, the corresponding coding bit is changed from selecting a random number to selecting a new position according to formula (13) and formula (14). When updating the pheromone used in the rocket cluster, when each rocket generates a new position according to formula (12), its coding bit also calculates its change value according to formula (13) and formula (14). The timing of pheromone update is selected after the rocket cluster completes the hunting and before updating the cluster state in each iteration. The pheromone is globally updated according to formula (15) using the position information of the leader rocket and its objective function value. The continuously updated pheromone represents the attack "wisdom" formed by the rocket cluster in the entire process of attacking the target, which is conducive to the intelligent optimization algorithm to better learn the solution space.

Claims

1. A method for allocating firepower of a long-range guided rocket cluster based on a wolf pack algorithm, characterized in that: The steps include: Step 1: Establish a specific scenario for long-range guided rocket cluster combat to attack multiple targets; Step 1-1: Create the following scene: (1) There are a total of V-type rockets in the long-range guided rocket cluster, and the number of Z-type rockets is , z=1,2,…v, and , m represents the total number of rockets; (2) Each rocket is a combat unit, carrying one type of warhead; (3) The total number of targets is n, and the value of the g-th target is ; is the single kill probability of the z-th rocket attacking the g-th target; (4) Decision variables is the number of z-th rockets used to attack the g-th target, , the probability of the z-th rocket killing the g-th target for: (1) (5) The combined kill probability of all V-shaped rockets on the g-th target for: (2) (6) The mathematical model of firepower distribution is expressed as follows: (3) in, is the maximum target value benefit, for The mean of Step 1-2: The constraints of the firepower allocation mathematical model include: (1) Task completion constraints; (4) Where: The total number of rockets allocated to the g-th target; (2) Effective killing constraints; (5) Where: is the preset killing probability threshold of the g-th target; (3) Attack consumption constraints; (6) Step 1-3: The long-range guided rocket cluster model features include the following aspects: (1) Ensure that every target can be effectively killed; By measuring whether the comprehensive killing probability of the target exceeds the preset killing probability threshold, it is determined whether the target is effectively killed. If it is lower than the preset killing probability threshold, the allocation to the target is considered invalid. (2) Set target value , ensuring the target value High goals are assigned priority; (3) Size Compare right The value has a greater impact. The smaller the value, the greater the average value of the comprehensive kill probability; (4) Guarantee target When the target of the assigned rocket is the same, that is, Same, select A large rocket combination makes the overall kill probability as high as possible; Step 2: Establish the movement rules and intelligent behavior of long-range guided rocket clusters based on the wolf pack algorithm; Step 2-1: In the long-range guided rocket cluster firepower allocation problem, an integer-based encoding method is used, as follows: (1) Allocation plan ,variable is an integer between 0 and n, Indicates that the Rockets are allocated to goals, Indicates that the j-th rocket is not assigned to any target; (2) Use the rocket position to represent a candidate allocation scheme: Assume that the rocket position vector dimension is also , the total number of artificial wolf rockets is ,exist Rocket in European Space Location , For the Rockets in the The position in the dimensional variable space; (3) The amount of target information detected by the rocket , that is, the objective function value, the rocket With Rocket Distance Manhattan distance for the position encoding of the two: (7) Step 2-2: Define the encoding method in step 2-1 as follows: (1) Define the walk operator , the position of rocket i is , is the set of coding bits, i.e., the attack range of the artificial wolf rocket; r is the number of coding bits to be changed, i.e., the movement step length of the rocket; the walk operator represents Randomly select r code bits to form a set ,Will Change to a random number selected from the j-dimensional variable space; (2) Define the summon operator , the position of rocket i is , Rocket location for artificial wolf and Rocket's position as the leader of the WPA A set of different coding bits, which is not an empty set. The rocket of the alpha wolf in WPA is referred to as the "alpha wolf rocket"; the set for: (8) In the formula, k represents the number of iterations, the initial value of k is 1; null represents a null value; Indicates the j-th dimension value of the alpha wolf position; Summon Counter For Randomly select r code bits to form a set , and The value is changed according to formula (9), which means that rocket i receives part of the information transmitted by the leader rocket: (9) Step 2-3: Establish behavior rules and intelligent behaviors of long-range guided rocket clusters based on wolf pack algorithm: (1) Rules of conduct for the Alpha Wolf Rocket: The rocket with the best objective function value is the leader rocket. During the iteration process, the leader rocket is replaced according to the objective function. The leader rocket does not perform wandering, summoning, or siege behaviors. (2) Wandering behavior: The best rocket is selected as the alpha rocket, and all rockets except the alpha rocket are used as the alpha rockets in WPA, referred to as "alpha rockets". Take a tentative step in h directions, that is, to probe the position of the Wolf rocket Execute h walk operators ; Then the obtained h new positions Make a greedy decision, that is, choose the position with the best objective function value and better than the original position objective function value as the new position of the rocket; let the objective function values ​​corresponding to the positions of the exploratory wolf rocket i and the head wolf rocket be and , the number of walks is T, and the maximum number of walks is Repeat the above process until or ; (3) summoning behavior; Define that except for the Alpha Wolf rocket, all the rockets in the long-range guided rocket cluster are the rockets of the Wolf in WPA, referred to as "Wolf Rockets". The Wolf Rockets are summoned by the Alpha Wolf with a running step length of Rocket to the location of the wolf leader Closer, that is, the position of the Wolf Rocket Executes a summon operator once ;like Execute the walk operator when it is an empty set ;like ,but , Wolf Rocket i replaces Wolf Rocket for summoning; if , then Wolf Rocket i continues to charge until the distance between Wolf Rocket i and the Alpha Wolf Rocket is less than the judgment distance ; (4) Behavior of siege; The location of the Wolf's rocket The position of the target, the position of the rockets involved in the siege According to formula (10), the position is transformed to obtain the new position: compare the target information detected by the rocket at the new and old positions before and after the siege and make a greedy decision; (10) Walking step length , running stride length , Siege Step Length All are integers, indicating the precision of the artificial wolf search; is an intermediate variable; (5) Rocket cluster update mechanism; According to the rocket cluster update mechanism, the group is updated, that is, eliminated Rockets, is a random integer between [N / (2U), N / U], and U is the update scale factor; the new leader rocket obtains part of the coding position of the previous leader rocket, and the new rocket position Calculated by formula (11): (11) In the formula, The location of the Alpha Wolf rocket; ; The number of coded bit changes L is calculated by formula (12): (12) Where: To round down; is the maximum number of iterations of the algorithm; represents the number of encoding bits of the target g changed at the kth iteration; Step 3: Establish a target allocation scheme based on ant colony algorithm; Step 3-1: Establish a target allocation scheme based on the ant colony algorithm to replace the wandering operator, and improve the firepower allocation and reaction speed of the long-range guided rocket cluster based on the wolf pack algorithm; According to the ant colony algorithm, starting from the first rocket, a target is selected from all targets and assigned to the rocket; then for the second rocket, a target is selected from the target set currently assigned and assigned to the rocket; the assignment is carried out in this order to guide the completion of all target assignments; at this time, the specific rules inspired by pheromones in the ant colony algorithm are as follows: (1) State transition rules; Select target g according to pseudo-random rule and assign it to the u-th rocket; (13) Where: is the pheromone between the u-th rocket and the target t at the k-th iteration; For heuristic information related to the problem, in the firepower allocation problem, is the product of the probability of the u-th rocket killing target t and the value of target t; and They are and The relative importance of; q is a random number uniformly distributed between 0 and 1; is a specified constant; G is a random proportion rule according to the following Choose a goal from the list; (14) Where: is the currently allocatable target set; is the product of the probability of the u-th rocket killing target g and the value of target g; is the pheromone between the u-th rocket and the target g at the k-th iteration; represents the random ratio rule for selecting target g for the u-th rocket at the k-th iteration; (2) Pheromone update rules; After one iteration process is completed, the pheromone is adjusted according to the global update rule of formula (15): (15) In the formula, Select the pheromone increment corresponding to target g for the u-th rocket; It is the volatile factor of pheromone; is the pheromone between the u-th rocket and the target g at the k+1th iteration; (16) In the formula, Q is the pheromone intensity, which is a constant; is the optimal value of the objective function for the kth iteration; is the corresponding optimal solution; Step 3-2: The specific method of adding pheromone guidance is that when the rocket is wandering and the rocket position executes the wandering operator, the corresponding coding bit is changed from selecting a random number to selecting a new position according to formula (13) and formula (14); when updating the pheromone used in the rocket cluster, when each rocket generates a new position according to formula (12), its coding bit calculates its change value according to formula (13) and formula (14); the timing of pheromone update is selected after the rocket cluster completes the hunt and before updating the cluster state in each iteration, and the pheromone is globally updated according to formula (15) using the position information of the leader rocket and its objective function value.

2. The method for allocating firepower of a long-range guided rocket cluster based on a wolf pack algorithm according to claim 1, characterized in that: The pheromone volatile factor The value range is .

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