CN116205102A - System elastic recovery method and system based on improved ant colony algorithm - Google Patents
System elastic recovery method and system based on improved ant colony algorithm Download PDFInfo
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Abstract
The invention discloses a system elasticity recovery method and system based on an improved ant colony algorithm, which are used for establishing a simulation model of multi-wave combat to simulate multi-wave combat of two sides of a friend or foe, wherein the simulation model comprises attack sides of different categories and different damage degrees; own side defense system module; a tamper resistant module; and (5) maintaining the recovery module. And calculating the war time importance of the equipment node according to the battlefield environment under the current wave number, and calculating the maintenance sequence according to the improved ant colony algorithm. The principle and the characteristics of the ant colony algorithm are analyzed and summarized, and the ant colony algorithm is improved from three angles of grid search algorithm, non-uniform initial pheromone setting and set point classification in the group based on the characteristics and the defects of the ant colony algorithm. And finally, the improved ant colony algorithm is used as a recovery algorithm of the system to help the system to determine a recovery path in multi-wave combat, so that the system has better elastic performance. The algorithm of the invention is improved reasonably, and the effectiveness of the invention is verified by the calculation result.
Description
Technical Field
The invention relates to the field of optimization of system elastic recovery, in particular to a system elastic recovery method and system based on an improved ant colony algorithm.
Background
By "elastic" is meant a capability or quality characteristic that the system exhibits to cope with various disturbances, variations, and the ability of the system to predict, fight, absorb, react, adapt and recover from external disturbances. Since the "911" event, the international society has paid great attention to the study of social security and disaster response, and the elasticity has become a new research hotspot internationally. In the future, "elasticity" will be an important guideline and performance index throughout the life cycle of a weapon equipment system, which will be implemented from the design stage.
In recent years, students have had some achievements in optimizing the elastic recovery problem with ant colony algorithms. Liu Jiaguo and the like are used for researching faults of the power distribution network through an improved ant colony algorithm, so that the system can adaptively perform a switching optimization method, and the elasticity of the power distribution network is improved. Ma Xuesen and the like are researched aiming at the energy consumption problem of the wireless sensor network, and a recovery path optimization strategy about dead nodes is provided based on an ant colony algorithm, so that the transmission energy consumption is effectively reduced. The defense system with elasticity can be better adapted to rapid, changeable, uncertain and vigorous battlefield environments, and meets the combat demands. Therefore, an excellent elastic recovery strategy is of paramount importance. How to solve the system elasticity optimization problem by optimizing and improving the traditional ant colony algorithm becomes a technical problem to be solved.
Disclosure of Invention
The invention aims to: the invention aims to provide a system elastic recovery method and system based on an improved ant colony algorithm, which combines the characteristics of multi-wave combat, determines the optimal value of the initial parameter of the ant colony algorithm through grid search, determines a better maintenance path through the advantage of the improved ant colony algorithm of strong optimizing capability on an objective function, strengthens the maintenance bias of combat nodes, and solves the problem of maintenance recovery paths of a ship system under the multi-wave combat.
The technical scheme is as follows: the invention provides a system elasticity recovery method based on an improved ant colony algorithm, which comprises the following steps:
(1) Initializing attack information of an attacker, and constructing a complete multi-wave attack chain of the attacker;
(2) Initializing own defense system, and constructing a complete own defense system against attack of an attacker;
(3) Calculating the war time importance of the own equipment node according to the battlefield environment under the current wave number, sequencing the war time importance of the own equipment node from high to low, and selecting the own equipment node for defense according to the war time importance;
(4) In the beginning stage of the wave attack, determining the current efficiency level E of the own equipment node, and countering the detection capability of the own combat node with the anti-reconnaissance capability of the attack on the basis of the efficiency level E, wherein the interception capability of the own combat node is countered with the striking capability of the attack, so that the wave attack against the attack with the successful downward burst prevention is jointly obtained;
(5) According to attack of an attack party with successful burst prevention, explosive shock waves received by each equipment node of the own party are calculated;
(6) Determining a own node damage matrix under the current wave attack according to the own explosion shock wave, and obtaining a latest own node performance level matrix E by making a difference with the own node performance level E before explosion n+1 ；
(7) Calculating an output maintenance sequence through an improved ant colony algorithm to obtain an optimal maintenance path conforming to a recovery strategy;
(8) Updating the own node efficiency level according to the optimal maintenance path;
(9) Judging whether the multi-wave attack is finished or not, if not, returning to the step (3), and if so, outputting the elasticity of the system.
Further, in the step (3), the war importance calculation formula of the own equipment node under the current wave number is as follows:
in the formula, wartime_Z i For war time importance, Z i Combat importance matrix for node i, type k And s represents the attack type of the attack and is the number of attacks in the current wave number.
Further, in the step (5), the formula of the blast shock wave received by each equipment node of the own party is as follows:
wherein DeltaP is an air shock wave overpressure value, R' is an equivalent distance, R is a distance from a node to an explosion center, and W TNT TNT equivalent of a weapon for an attacker.
Further, in the step (7), the improved ant colony algorithm specifically includes the following steps:
(7.1) setting initial parameters of an ant colony algorithm, wherein the initial parameters comprise maximum iteration times and an optimal path ant group, and the algorithm receives the initial parameters in multi-wave combat, including maintenance node information, efficiency level matrix and combat type nodes;
(7.2) initializing ant groups, and constructing an unevenly distributed initialization pheromone concentration matrix;
initializing small ants in the ant group, inheriting a tabu table in the group, setting a departure point classification rule, generating an initial node, and calculating the time required for the small ants to move to the current wave initial node according to the ending position of the previous wave;
(7.4) circularly determining the moving direction of the small ants, making constraint conditions and judging whether the next node can be successfully maintained;
(7.5) if the constraint condition meets the next node maintenance requirement, judging that the maintenance is successful, updating the constraint condition and the in-group tabu list, and if the constraint condition does not meet the next node maintenance requirement, generating a sub feasible solution of the small ants;
(7.6) judging whether the maintenance tasks in the ant group are completed or not, if not, turning to the step (7.3), and generating a new sub-feasible solution; if the maintenance task in the group of ants is finished or the number of ants in the group reaches 5, carrying out the next step;
(7.7) combining sub-feasible solutions of all ants in the ant subgroup to form a complete feasible solution, wherein the feasible solution is a maintenance path, calculating the quality degree of the feasible solution, and updating the global optimal path ant subgroup if the current solution is better than the global optimal solution;
and (7.8) updating the pheromone concentration matrix according to the feasible solution conditions of all ant groups in the current iteration, judging whether the maximum iteration times are reached, if not, turning to the step (7.2), and if so, outputting the global optimal solution and ending the calculation.
The invention correspondingly provides a system elastic recovery system based on an improved ant colony algorithm, which comprises an attacker initialization module, a host initialization module, a war time importance determining module, a countermeasure module, a damage evaluation module, a primary updating module, an improved ant colony algorithm module, a secondary updating module and a judging module;
the attacker initializing module is used for initializing attacker attack information and constructing a complete multi-wave attacker attack chain;
the own initialization module is used for initializing own defense system and constructing a complete own defense system against attack of an attacker;
the time-of-war importance determining module is used for calculating the time-of-war importance of the own equipment node according to the battlefield environment under the current wave number, sequencing the time-of-war importance of the own equipment node from high to low, and selecting the own equipment node for defense according to the time-of-war importance;
the countermeasures module is used for determining the current efficiency level E of the own equipment node at the beginning stage of the wave attack, countermeasures the detection capability of the own combat node with the anti-reconnaissance capability of the attack on the basis of the efficiency level E, countermeasures the interception capability of the own combat node with the striking capability of the attack, and jointly obtains the attack of the wave attack against the success of the downward burst prevention;
the damage evaluation module is used for calculating the explosion shock waves received by each equipment node of the own side according to the attack of the attack party with successful burst prevention;
the primary updating module is used for determining a own node damage matrix under the current wave attack according to the own explosion shock wave, and obtaining a latest own node performance level matrix E by making a difference with the own node performance level E before explosion n+1 ；
The improved ant colony algorithm module is used for calculating an output maintenance sequence through an improved ant colony algorithm to obtain an optimal maintenance path conforming to a recovery strategy;
the secondary updating module is used for updating the own node performance level according to the optimal maintenance path;
the judging module is used for judging whether the multi-wave attack is finished or not, returning to the war time importance determining module if the multi-wave attack is not finished, and outputting the system elasticity if the multi-wave attack is finished.
Further, in the time of war importance determination module, a time of war importance calculation formula of own equipment node under the current wave number is as follows:
in the formula, wartime_Z i For war time importance, Z i Combat importance matrix for node i, type k And s represents the attack type of the attack and is the number of attacks in the current wave number.
Further, in the damage evaluation module, the formula of the explosion shock wave received by each equipment node of the own side is as follows:
wherein DeltaP is an air shock wave overpressure value, R' is an equivalent distance, R is a distance from a node to an explosion center, and W TNT TNT equivalent of a weapon for an attacker.
Further, the improved ant colony algorithm module comprises a grid searching and initializing unit, a non-uniform initial pheromone unit, an intra-group departure point classifying unit, a circulating direction unit, a constraint condition judging unit, a maintenance task judging unit, a feasible solution constructing unit and an optimal solution outputting unit;
the grid searching and initializing unit is used for setting initial parameters of an ant colony algorithm, including maximum iteration times and optimal path ant groups, and the algorithm receives the initial parameters in multi-wave combat, including maintenance node information, efficiency grade matrix and combat nodes;
the non-uniform initial pheromone unit is used for initializing the ant group and constructing a non-uniformly distributed initial pheromone concentration matrix;
the in-group departure point classification unit is used for initializing small ants in the ant group, inheriting the in-group tabu table, setting a departure point classification rule, generating an initial node, and calculating the time required for the small ants to move to the current wave initial node according to the ending position of the previous wave;
the circulating direction unit is used for circularly determining the moving direction of the small ants, making constraint conditions and judging whether the next node can be successfully maintained;
the constraint condition judging unit is used for judging constraint conditions, judging that the maintenance is successful if the constraint conditions meet the maintenance requirement of the next node, updating constraint conditions and the tabu table in the group, and generating sub-feasible solutions of the small ants if the constraint conditions do not meet the maintenance requirement of the next node;
the maintenance task judging unit is used for judging whether the maintenance task in the group of the ant subgroup is completed or not, and if not, the maintenance task is transferred to the classification unit of the departure point in the group to generate a new sub-feasible solution; if the maintenance task in the group of ants is finished or the number of ants in the group reaches 5, a feasible solution unit is built;
constructing a feasible solution unit for combining sub-feasible solutions of all ants in the ant subgroup to form a complete feasible solution, wherein the feasible solution is a maintenance path, calculating the quality degree of the feasible solution, and updating the global optimal path ant subgroup if the current solution is better than the global optimal solution;
the output optimal solution unit is used for updating the pheromone concentration matrix according to the feasible solution conditions of all ant groups in the current iteration, judging whether the maximum iteration times are reached, if not, turning to the non-uniform initial pheromone unit, and if so, outputting the global optimal solution and ending the calculation.
The beneficial effects are that: compared with the prior art, the invention has the remarkable advantages that: by establishing a multi-wave combat simulation model, multi-wave combat of both sides of a friend or foe is simulated, wherein the multi-wave combat comprises attack sides of different categories and different damage degrees. From the characteristics and defects of the ant colony algorithm, the ant colony algorithm is improved from three angles of a grid search algorithm, a non-uniform initial pheromone and classification of the occurrence points in the group. The improved ant colony algorithm is used as a recovery algorithm of the system to help the system to determine the recovery path in multi-wave combat, so that the optimizing capability of the objective function is enhanced, the maintenance path is determined more quickly, and the system has better elastic performance.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a diagram of a full-elastic process of the overall performance level of the system in a torpedo-based attack scenario according to the present invention;
FIG. 3 is a diagram of a total importance degree full-elastic process of the system in a torpedo attack based scene in the invention;
FIG. 4 is a diagram of a full resilience process based on the overall performance level of the system in an aircraft/missile scenario in accordance with the present invention;
FIG. 5 is a diagram of the overall system importance fully elastic process based on an aircraft/missile scene in accordance with the present invention;
FIG. 6 is a diagram of a system total performance level full-elastic process in a scenario based on a group of unmanned aerial vehicles in the present invention;
fig. 7 is a diagram of a total importance degree full-elastic process of the system in the unmanned aerial vehicle group attack scene.
Detailed Description
The following describes specific embodiments of the present invention with reference to the drawings.
The invention provides a system elasticity recovery method based on an improved ant colony algorithm, referring to fig. 1, comprising the following steps:
(1) Initializing attack information of an attacker, determining attack wave times of the attacker, the types, the quantity, the drop points and TNT equivalent information of each wave time of attack, and constructing a complete multi-wave attack chain by using the anti-reconnaissance capability and the striking capability of the attack wave times.
According to different weapon attack modes, three attack types of underwater torpedoes, high-altitude planes/missiles and low-altitude small unmanned aerial vehicles are sequentially selected from large to small in terms of explosion equivalent and interception difficulty to serve as simulation objects, and according to different attack types, an attacker attack matrix with multiple wave times comprising different explosion equivalent and landing points is constructed.
(2) Initializing a host defense system, determining all equipment node information of the host, including three-dimensional node coordinates, node categories (basic nodes, combat nodes and secondary nodes), detection/interception capability of the combat nodes, node importance and node initial effectiveness level matrix E, and constructing a complete host defense system against attack of an attacker.
Equipment node information, three main types of common classification base nodes of equipment nodes, combat nodes and secondary nodes: base node: the foundation node for supporting the operation of the ship has higher importance in any operational environment, but has no capability of protecting the ship; fight type node: refers to an equipment node with detection or interception capability for an attacker attack. According to different capacities of the fight type nodes, 5 radar devices are selected for detecting aerial missiles at high altitude and unmanned aerial vehicle groups at low altitude respectively, and 5 sonar devices are used for detecting underwater torpedo attacks, so that the tasks of the nodes are more definite, and the maintenance strategy adjusted according to battlefield environments after ships are injured is more targeted. Secondary node: the method is not important in tasks, is responsible for daily work of ships, has no effect in an emergency battlefield environment, and has low battlefield importance.
Detection/interception capability of combat node: the combat type node comprises a detection type node and an interception type node, and the capability of the detection type node and the interception type node determines whether an attacker can successfully detect and intercept the attack and wins the fight task. The node capability is represented by a floating point number of 0-1, with higher values representing greater capabilities for probing or interception.
Node importance: regardless of the restoration strategy, all the damaged nodes are selectively maintained under various constraint conditions, and the bias of the selection is determined by different importance values of the equipment nodes so as to output a maintenance path which accords with the restoration strategy concept as much as possible through an algorithm.
Node initial performance level matrix E: in order to better simulate the damage and repair condition of the system node in the war, a performance grade matrix of the system node is introduced, after the system is attacked, the performance grade of equipment is correspondingly reduced according to the damage degree, and after a certain maintenance, the performance grade of the node is restored to a certain value or even completely restored to the highest value.
(3) And calculating the war time importance of the own equipment node according to the battlefield environment under the current wave number, sequencing the war time importance of the own equipment node from high to low, and selecting the own equipment node for defense according to the war time importance.
The node war time importance is a device node maintenance priority measurement standard based on an Attack Defense Recovery Strategy (ADRS), and the node importance is a key index for guiding an algorithm to conduct objective function optimization. According to the method, importance is adjusted for each node according to the current combat environment, and the high importance indicates that the node has advantages to the current combat situation, so that the system can be helped to resist the attack coming by an attacker; a low importance indicates that the task type of the node does not satisfy the current operational environment or does not have operational capability itself. The war importance calculation formula of own equipment node under the current wave number is as follows:
in the formula, wartime_Z i For war time importance, Z i Combat importance matrix for node i, type k And s represents the attack type of the attack and is the number of attacks in the current wave number.
(4) And in the beginning stage of each wave attack, determining the current efficiency level E of the own equipment node by a uniform line, and countering the detection capability of the own combat type node with the counterreconnaissance capability of the attack by the attack party on the basis of the efficiency level E to jointly obtain the attack of the attack party with successful downward burst prevention of the wave attack by the interception capability of the own combat type node with the attack capability of the attack party.
The process of the antagonism of the two sides of the friend and foe is to simulate the multi-wave antagonism and damage process of the two sides of the friend and foe. Challenge refers to the comparison of the destructive power of an attacker with the defensive power between the warfare nodes of the own at the beginning of each wave of attack, which is related to the attack type of the attacker, the attack success rate and the detection or interception success rate, current efficacy level of the warfare nodes of the own, so that the defensive system is a multiple function of whether the challenge task of the attack of the attacker of the current wave is successful or not.
(5) According to the attack of the attack party with the successful burst prevention, according to the landing point of the attack party and TNT equivalent information, the explosion shock wave received by each equipment node of the own party is calculated.
The formula of the explosion shock wave received by each equipment node of the own party is as follows:
wherein DeltaP is an air shock wave overpressure value, R' is an equivalent distance, R is a distance from a node to an explosion center, and W TNT TNT equivalent of a weapon for an attacker.
From the relation between the overpressure of the shock wave and the injury radius, the farther from the explosion center under the condition of the same explosion equivalent, the less the equipment node is affected by the shock wave. And converting the overpressure of the shock waves received by the system nodes into injury grades, and refining the damage condition of the nodes. The details are shown in Table 1 below.
Table 1 damage assessment table
(6) Determining a own node damage matrix E under the current wave attack according to own explosion shock waves Destroy And the latest own node performance level matrix E is obtained by making a difference with the own node performance level E before explosion n+1 。
The calculation of the current node performance level is obtained by the difference between the performance level of the system in a state of no attack and the damage of the attack by the attacker of the current subsystem.
(7) And calculating an output maintenance sequence through an improved ant colony algorithm to obtain an optimal maintenance path conforming to a recovery strategy.
Although the ant colony algorithm has the characteristics of easy fusion, strong search guidance, strong algorithm robustness and the like, the ant colony algorithm still has some non-negligible defects, and the improved ant colony algorithm is improved from the following three aspects: a grid search algorithm, a non-uniform initial pheromone and a set of in-group departure point classification are set;
and (7.1) setting initial parameters of an ant colony algorithm, wherein the initial parameters comprise the maximum iteration times and an optimal path ant group, and the algorithm receives the initial parameters in multi-wave combat, including maintenance node information, efficiency level matrix and combat type nodes.
Because parameters related to pheromones control state transition probability, pheromone increment and the like, the algorithm always runs through, so that the ant colony algorithm parameter determination and how to determine the key direction of the ant colony algorithm improvement always are determined. In this way, the resulting parameter combinations are m=50, q=20, α=4.0, β=0.4, ρ=0.9.
(7.2) initializing ant groups, and constructing an unevenly distributed initialization pheromone concentration matrix.
Aiming at the problem of weak initial guidance of the ant colony algorithm, the initial pheromone is set and analyzed, and the solving speed of the initial pheromone accelerating algorithm is reasonably distributed. The non-uniform initial pheromone is arranged at the beginning stage of the ant colony algorithm, so that the initial optimization of ants has directivity and the convergence speed is increased.
According to the war time importance of the nodes, a non-uniform pheromone distribution mode is adopted, so that the initial pheromone concentration of the equipment nodes with high importance is high, the transfer probability of other nodes to the nodes with high importance is increased, ants are prevented from blindly searching by the node i with the importance, and the calculation formula of the non-uniform pheromone concentration matrix of the nodes is as follows:
in the above
Initial pheromone concentration representing node i to node j, wartime_z i Representing the war time importance of node i, subtracting 0.1. Wartime_Z from the equation j The goal of (a) is to reduce the probability of a high importance node stepping toward a low importance node.
And (7.3) initializing the ants in the ant group, inheriting the tabu table in the group, setting a departure point classification rule, generating an initial node, and calculating the time required for the ants to move to the current initial node according to the ending position of the previous wave.
Setting the classification of starting points in the group, and maintaining the combat type nodes preferentially to ensure the combat capability of the system, and reducing the damage degree of the subsequent wave number so as to lighten the maintenance pressure. The specific description of the strategy is as follows: according to the operational scene, firstly determining operational nodes PowerPoints conforming to the current environment, when the ants in the group start from the initial starting point, selecting one node randomly from the PowerPoints as an initial starting point, when the ants reach a stopping condition and the nodes are not maintained, sending out a second ant to continue maintenance tasks, and keeping a maintenance node tabu table of the first ant, meanwhile, selecting one node randomly from the PowerPoints still preferentially by the initial nodes of the second ant, and if no PowerPoints are available, namely, all PowerPoints in the current wave time are maintained, selecting initial nodes randomly from the rest other nodes, and finishing maintenance of the next ants in the group similarly until maintenance of all damaged nodes is completed or the number of ants in all groups is maximum.
And (7.4) circularly determining the moving direction of the small ants, making constraint conditions and judging whether the next node can be successfully maintained.
And (7.5) if the constraint condition meets the next node maintenance requirement, judging that the maintenance is successful, updating the constraint condition and the in-group tabu list, and if the constraint condition does not meet the next node maintenance requirement, generating a sub feasible solution of the small ant.
(7.6) judging whether the maintenance tasks in the ant group are completed or not, if not, turning to the step (7.3), and generating a new sub-feasible solution; if the maintenance task in the group of ants is completed or the number of ants in the group reaches 5, the next step is carried out.
And (7.7) combining sub-feasible solutions of all ants in the ant group to form a complete feasible solution, wherein the feasible solution is a maintenance path, calculating the quality degree of the feasible solution, and updating the global optimal path ant group if the current solution is better than the global optimal solution.
The following formula is adopted for calculating the quality of the feasible solution:
and (7.8) updating the pheromone concentration matrix according to the feasible solution conditions of all ant groups in the current iteration, judging whether the maximum iteration times are reached, if not, turning to the step (7.2), and if so, outputting the global optimal solution and ending the calculation.
(8) And updating the own node performance level according to the optimal maintenance path.
The basis for updating the own system performance level is the system maintenance condition under the current wave number.
(9) Judging whether the multi-wave attack is finished or not, if not, returning to the step (3), and if so, outputting the elasticity of the system. The total number of wave times is determined according to the wave times of attack of the attacker, and if all the wave times of attack and maintenance after the attack are finished, the simulation of the model is finished.
Examples
Quantitative analysis is carried out on the elastic index change of the ship system in the multi-wave combat process under different scenes:
and bringing the importance degree of the nodes into the formula, and solving the elastic change of the ship system in each wave of combat according to the formula.
The overall elasticity process diagram of the overall efficiency level of the system based on the torpedo attack scene is shown in fig. 2, the overall importance of the system based on the torpedo attack scene is shown in fig. 3, and the overall elasticity of the system based on the torpedo attack scene is compared with the following table 2:
TABLE 2 elastic comparison of systems in Torpedo attack scenarios
The overall performance level overall elasticity process diagram based on the system in the aircraft/missile scene is shown in fig. 4, the overall importance overall elasticity process diagram based on the system in the aircraft/missile scene is shown in fig. 5, and the comparison of the system elasticity based on the aircraft/missile scene is shown in the following table 3:
TABLE 3 elastic comparison of systems in aircraft/missile attack scenarios
The overall elasticity process diagram of the overall system performance level under the attack scene based on the unmanned aerial vehicle group is shown in fig. 6, the overall elasticity process diagram of the overall system importance under the attack scene based on the unmanned aerial vehicle group is shown in fig. 7, and the comparison of the system elasticity under the attack scene based on the unmanned aerial vehicle group is shown in the following table 4:
TABLE 4 elastic comparison of systems in unmanned aerial vehicle group attack scenarios
The experimental group based on ADRS, which is obtained from the above tables 2, 3 and 4, is the traditional ant colony algorithm or the improved ant colony algorithm, and can ensure that the system has no damaged node after the maintenance of the third wave and intercepts the attack of the following wave, and the damage degree of the whole process system is effectively reduced compared with the total damage of the GRS-traditional ant colony algorithm; meanwhile, the system based on the improved ant colony algorithm in the aspect of elasticity improvement has more obvious improvement in each wave of combat, and the system based on the ADRS-improved ant colony algorithm has better elasticity.
Claims (10)
1. The system elasticity recovery method based on the improved ant colony algorithm is characterized by comprising the following steps of:
(1) Initializing attack information of an attacker, and constructing a complete multi-wave attack chain of the attacker;
(2) Initializing own defense system, and constructing a complete own defense system against attack of an attacker;
(3) Calculating the war time importance of the own equipment node according to the battlefield environment under the current wave number, sequencing the war time importance of the own equipment node from high to low, and selecting the own equipment node for defense according to the war time importance;
(4) In the beginning stage of the wave attack, determining the current efficiency level E of the own equipment node, and countering the detection capability of the own combat node with the anti-reconnaissance capability of the attack on the basis of the efficiency level E, wherein the interception capability of the own combat node is countered with the striking capability of the attack, so that the wave attack against the attack with the successful downward burst prevention is jointly obtained;
(5) According to attack of an attack party with successful burst prevention, explosive shock waves received by each equipment node of the own party are calculated;
(6) Determining a own node damage matrix under the current wave attack according to the own explosion shock wave, and obtaining a latest own node performance level matrix E by making a difference with the own node performance level E before explosion n+1 ；
(7) Calculating an output maintenance sequence through an improved ant colony algorithm to obtain an optimal maintenance path conforming to a recovery strategy;
(8) Updating the own node efficiency level according to the optimal maintenance path;
(9) Judging whether the multi-wave attack is finished or not, if not, returning to the step (3), and if so, outputting the elasticity of the system.
2. The improved ant colony algorithm-based system elasticity restoration method according to claim 1, wherein in the step (3), the war importance calculation formula of the own equipment node at the current wave number is as follows:
in the formula, wartime_Z i For war time importance, Z i Combat importance matrix for node i, type k And s represents the attack type of the attack and is the number of attacks in the current wave number.
3. The method of recovering system elasticity based on the improved ant colony algorithm according to claim 1, wherein in the step (5), the formula of the blast shock wave received by each equipment node of the own party is as follows:
wherein DeltaP is an air shock wave overpressure value, R' is an equivalent distance, R is a distance from a node to an explosion center, and W TNT TNT equivalent of a weapon for an attacker.
4. The method of system elastic recovery based on the improved ant colony algorithm according to claim 1, wherein in step (7), the improved ant colony algorithm specifically comprises the steps of:
(7.1) setting initial parameters of an ant colony algorithm, wherein the initial parameters comprise maximum iteration times and an optimal path ant group, and the algorithm receives the initial parameters in multi-wave combat, including maintenance node information, efficiency level matrix and combat type nodes;
(7.2) initializing ant groups, and constructing an unevenly distributed initialization pheromone concentration matrix;
initializing small ants in the ant group, inheriting a tabu table in the group, setting a departure point classification rule, generating an initial node, and calculating the time required for the small ants to move to the current wave initial node according to the ending position of the previous wave;
(7.4) circularly determining the moving direction of the small ants, making constraint conditions and judging whether the next node can be successfully maintained;
(7.5) if the constraint condition meets the next node maintenance requirement, judging that the maintenance is successful, updating the constraint condition and the in-group tabu list, and if the constraint condition does not meet the next node maintenance requirement, generating a sub feasible solution of the small ants;
(7.6) judging whether the maintenance tasks in the ant group are completed or not, if not, turning to the step (7.3), and generating a new sub-feasible solution; if the maintenance task in the group of ants is finished or the number of ants in the group reaches 5, carrying out the next step;
(7.7) combining sub-feasible solutions of all ants in the ant subgroup to form a complete feasible solution, wherein the feasible solution is a maintenance path, calculating the quality degree of the feasible solution, and updating the global optimal path ant subgroup if the current solution is better than the global optimal solution;
and (7.8) updating the pheromone concentration matrix according to the feasible solution conditions of all ant groups in the current iteration, judging whether the maximum iteration times are reached, if not, turning to the step (7.2), and if so, outputting the global optimal solution and ending the calculation.
5. The system elastic recovery system based on the improved ant colony algorithm is characterized by comprising an attacker initialization module, a host initialization module, a war time importance determining module, a countermeasure module, a damage evaluation module, a primary updating module, an improved ant colony algorithm module, a secondary updating module and a judging module;
the attacker initializing module is used for initializing attacker attack information and constructing a complete multi-wave attacker attack chain;
the own initialization module is used for initializing own defense system and constructing a complete own defense system against attack of an attacker;
the time-of-war importance determining module is used for calculating the time-of-war importance of the own equipment node according to the battlefield environment under the current wave number, sequencing the time-of-war importance of the own equipment node from high to low, and selecting the own equipment node for defense according to the time-of-war importance;
the countermeasures module is used for determining the current efficiency level E of the own equipment node at the beginning stage of the wave attack, countermeasures the detection capability of the own combat node with the anti-reconnaissance capability of the attack on the basis of the efficiency level E, countermeasures the interception capability of the own combat node with the striking capability of the attack, and jointly obtains the attack of the wave attack against the success of the downward burst prevention;
the damage evaluation module is used for calculating the explosion shock waves received by each equipment node of the own side according to the attack of the attack party with successful burst prevention;
the primary updating module is used for determining a own node damage matrix under the current wave attack according to the own explosion shock wave, and obtaining a latest own node performance level matrix E by making a difference with the own node performance level E before explosion n+1 ；
The improved ant colony algorithm module is used for calculating an output maintenance sequence through an improved ant colony algorithm to obtain an optimal maintenance path conforming to a recovery strategy;
the secondary updating module is used for updating the own node performance level according to the optimal maintenance path;
the judging module is used for judging whether the multi-wave attack is finished or not, returning to the war time importance determining module if the multi-wave attack is not finished, and outputting the system elasticity if the multi-wave attack is finished.
6. The system elastic recovery system based on the improved ant colony algorithm according to claim 5, wherein in the time of war importance determination module, the time of war importance calculation formula of the own equipment node at the current wave number is as follows:
in the formula, wartime_Z i For war time importance, Z i Combat importance matrix for node i, type k And s represents the attack type of the attack and is the number of attacks in the current wave number.
7. The system elastic recovery system based on the improved ant colony algorithm according to claim 5, wherein in the damage evaluation module, the formula of the blast shock wave received by each equipment node on the own side is as follows:
wherein DeltaP is an air shock wave overpressure value, R' is an equivalent distance, R is a distance from a node to an explosion center, and W TNT TNT equivalent of a weapon for an attacker.
8. The system elastic recovery system based on the improved ant colony algorithm according to claim 5, wherein the improved ant colony algorithm module comprises a grid searching and initializing unit, a non-uniform initial pheromone unit, an intra-group departure point classifying unit, a circulation direction unit, a constraint condition judging unit, a maintenance task judging unit, a feasible solution constructing unit and an optimal solution outputting unit;
the grid searching and initializing unit is used for setting initial parameters of an ant colony algorithm, including maximum iteration times and optimal path ant groups, and the algorithm receives the initial parameters in multi-wave combat, including maintenance node information, efficiency grade matrix and combat nodes;
the non-uniform initial pheromone unit is used for initializing the ant group and constructing a non-uniformly distributed initial pheromone concentration matrix;
the in-group departure point classification unit is used for initializing small ants in the ant group, inheriting the in-group tabu table, setting a departure point classification rule, generating an initial node, and calculating the time required for the small ants to move to the current wave initial node according to the ending position of the previous wave;
the circulating direction unit is used for circularly determining the moving direction of the small ants, making constraint conditions and judging whether the next node can be successfully maintained;
the constraint condition judging unit is used for judging constraint conditions, judging that the maintenance is successful if the constraint conditions meet the maintenance requirement of the next node, updating constraint conditions and the tabu table in the group, and generating sub-feasible solutions of the small ants if the constraint conditions do not meet the maintenance requirement of the next node;
the maintenance task judging unit is used for judging whether the maintenance task in the group of the ant subgroup is completed or not, and if not, the maintenance task is transferred to the classification unit of the departure point in the group to generate a new sub-feasible solution; if the maintenance task in the group of ants is finished or the number of ants in the group reaches 5, a feasible solution unit is built;
constructing a feasible solution unit for combining sub-feasible solutions of all ants in the ant subgroup to form a complete feasible solution, wherein the feasible solution is a maintenance path, calculating the quality degree of the feasible solution, and updating the global optimal path ant subgroup if the current solution is better than the global optimal solution;
the output optimal solution unit is used for updating the pheromone concentration matrix according to the feasible solution conditions of all ant groups in the current iteration, judging whether the maximum iteration times are reached, if not, turning to the non-uniform initial pheromone unit, and if so, outputting the global optimal solution and ending the calculation.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of claims 1 to 4 when the computer program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of claims 1 to 4.
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