CN112488314A - System elasticity recovery method and system based on improved genetic algorithm - Google Patents
System elasticity recovery method and system based on improved genetic algorithm Download PDFInfo
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Abstract
The invention discloses a system elasticity recovery method and a recovery system based on an improved genetic algorithm, wherein the recovery method comprises the following steps: initializing algorithm parameters, and encoding group individuals into maintenance paths of nodes; generating an initial population, wherein individuals of part of the initial population are randomly generated, and the rest of the initial population is generated according to a greedy strategy; evaluating the adaptive value of each generation of population individuals according to the fitness function, and recording the optimal individuals of the current population and the adaptive values thereof; maintaining elite individuals for the parent population, and generating a child population through inversion, crossing and sliding among groups; the parent individuals and the offspring individuals are matched with special genes to realize the first variation in the group, finally, the offspring population is generated, and a system elastic recovery system based on an improved genetic algorithm is provided. The recovery method and the recovery system of the invention are close to the requirements of the real environment, have high convergence rate and difficult precocity, and can effectively recover the elasticity of the system within a certain time.
Description
Technical Field
The invention relates to a system elasticity recovery method and a system, in particular to a system elasticity recovery method and a system based on an improved genetic algorithm.
Background
Elasticity, as well as security, is considered to be an attribute of a system, namely the ability of a system to recover its basic (or general) functionality after an attack (or disturbance) causes the system to be physically damaged and out of its control. An elastic system can better adapt to a rapidly changing, uncertain and violent confrontational environment. At present, the flexibility of the system is mostly established on the corresponding recovery method after the system is damaged, such as artificial repair. Then, in the case where most components need to be repaired, reasonable repair goal setting, repair resource scheduling, and optimal repair path planning become core problems for the repair strategy. The strength of the system elasticity in a short period is directly related to the quality of a recovery method, so that the establishment of the recovery method plays an important role in recovering the system elasticity performance.
At present, a swarm intelligent optimization algorithm is mostly adopted when solving the path planning problem or the recovery problem, and a Genetic Algorithm (GA) is taken as a main algorithm. The genetic algorithm has the characteristic of being capable of solving the global optimal solution of the optimization problem, but the traditional genetic algorithm has random initial population, low convergence rate and easy precocity. Meanwhile, in the case of solving the system elastic recovery problem by using the conventional genetic algorithm, the fitness function and the constraint condition are rarely set while the task importance, the spare part constraint and the time constraint are considered.
Disclosure of Invention
The purpose of the invention is as follows: the first purpose of the present invention is to provide a system elasticity recovery method based on an improved genetic algorithm, which is close to the real environment requirement, has a fast convergence rate, is not easy to get premature, and can effectively recover the system elasticity within a certain time.
The technical scheme is as follows: the invention discloses a system elasticity recovery method based on an improved genetic algorithm, which comprises the following steps:
(1) initializing algorithm parameters, and encoding group individuals into maintenance paths of nodes;
(2) generating an initial population, wherein individuals of part of the initial population are generated randomly, and the rest part of the initial population is generated by a greedy strategy according to a greedy model;
(3) constructing a fitness function based on task importance degree under the consideration of limited time, limited spare parts and grouped maintenance;
(4) evaluating the adaptive value of each generation of population individuals according to the fitness function, and recording the optimal individuals of the current population and the adaptive values thereof;
(5) algebra iteration, namely judging whether the adaptive value of the current generation optimal individual is higher than the adaptive value corresponding to the historical optimal individual, if so, updating the historical optimal adaptive value and the historical optimal individual, and continuing the following steps; if not, directly continuing the following steps;
(6) maintaining elite individuals for the parent population, and generating a child population through inversion, crossing and sliding among groups;
(7) matching the parent individuals and the offspring individuals with special genes to realize the first variation in the group, and finally generating an offspring population;
(8) judging whether an algorithm ending condition is met, if so, continuing the following steps, otherwise, performing population iteration number +1, and skipping to implement the step (4);
(9) outputting an optimal adaptive value and a corresponding optimal algebra, and drawing an optimal individual, namely an optimal maintenance path;
(10) and measuring the recovered system elasticity value.
The system elastic grouping parallel recovery method considering the time, spare parts and task importance can effectively transfer manpower and material resources, quickly recover the operation of system key tasks, maximally recover the elasticity of a damaged system in limited time, have good convergence and are not easy to get premature.
Further, the initialization algorithm parameters in step (1) mainly include: the method comprises the following steps of a node position matrix xy, a node number n, a node task importance z, node maintenance time h, the number c of nodes required to be maintained, a maintenance small group number s, the average walking speed v of maintenance personnel, a minimum maintenance node number minTour, a population size popSize and an iteration number numIter.
Selectively generating part of initial populations by using a greedy strategy, wherein the specific process is as follows:
(a) among the N damaged nodes, the first node repaired by each repair team is randomly generated.
(b) Setting a greedy degree model:
wherein: f (X, Y) represents the greedy degree of the node X to the node Y, degree (X) represents the task importance degree of the node X, distance (X, Y) represents the distance between the node X and the node Y, h (X) represents the time required by maintenance of the node X, and 70 is a coefficient obtained through a simulation experiment and used for generating a reasonable greedy degree;
(c) and selecting the next maintenance node of each group according to a greedy strategy, namely the next node is the node with the highest current priority. By analogy, a repair sequence for each subgroup, and thus a sequence for a single individual, can be obtained. Cycling M times, M initial individuals were generated and added to the initial population, where M < population size.
The specific method for considering the limited time and the limited spare parts in the step (3) is to add the importance, the time required for maintenance and the information of the spare parts required for maintenance into each node, and add the time constraint and the spare part constraint in a grouping parallel maintenance mode. Wherein, the importance, the time required by maintenance and the information of spare parts required by maintenance are shown in step (1), the time constraint is t <1800 (unit: s), and the maintenance team is considered to be maintained simultaneously, which specifically comprises the following steps:
ti＜1800(i＝1，2，3，4，5)
wherein each maintenance team maintains time tiComprises the following steps:
spare parts are constrained as:
in the formula, componentiAIndicates the number of parts A, components, used in total for the node repaired in actual repair by the ith repair teamiBIndicates the number of parts B, components, used in total for the node repaired in actual repair by the ith repair teamiCDenotes the number of parts C used in total for nodes where the repair is completed in actual repair by the i-th repair team, m denotes an index of a start node corresponding to the repair team repair, n denotes an index of an end node corresponding to the repair team repair, DjAIndicates the number of parts A used by the jth node maintained by the corresponding maintenance group, DjBIndicates the number of parts B used by the jth node in the maintenance of the corresponding maintenance group, DjCAnd the number of the parts C used for maintaining the jth node by the corresponding maintenance group is shown.
The task importance fitness function formula is as follows:
in the formula, totaldegree represents the total task importance degree completed by all maintenance subgroups, s represents the number of maintenance subgroups, k represents the node index, and z representsi[pRoute(k)]Indicating the mission importance of the kth node in the ith repair team repair sequence.
Further, when t isiNot less than 1800(i ═ 1, 2, 3, 4, 5) or componentiA> 12 or componentiB> 12 or componentiCWhen the maintenance time is more than 12, jumping out of the circulating body, calculating the total task importance of all maintenance groups, the number of the remained parts, the time for each group, and recording the maintenance sequence of each group, t when each group starts to maintainiAre all reset to 0.
The specific method for matching the special genes to realize the intra-group variation in the step (6) is to match the first nodes of the maintenance sequences in the elite individuals of the parent generation with the corresponding first nodes of the individual filial generation generated by inverting, crossing and sliding between the groups, and if the matching is successful, the next maintenance node of the first nodes of the maintenance sequences of the individuals of the parent generation is used as a variation point to be exchanged with the next maintenance node corresponding to the individual filial generation to generate the individual filial generation. Also known as first variation.
And (9) measuring the system elasticity by combining the task importance and the quotient elasticity model.
The system elastic recovery system based on the improved genetic algorithm comprises a node initialization module, a population initialization module, a variation iteration module and an elastic measurement module, wherein the node initialization module is used for initializing information such as node positions, spare parts needed by nodes, node task importance, node maintenance time, minimum maintenance node number and the like, the population initialization module is used for generating an initial population, the initial population is generated by combining a random generation and greedy strategy method, the variation iteration module is used for inheriting a parent elite individual and generating a filial population, the three variations and the first variation in the group are included, when the maximum iteration number is reached, the algorithm is ended, and the elastic measurement module is used for measuring the elastic value after the system is recovered.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
(1) the maintenance path is optimized by using an improved genetic algorithm, so that the optimal solution of the elastic recovery problem is improved, and the convergence speed is high;
(2) the improved genetic algorithm is used for optimizing the maintenance path, compared with the traditional genetic algorithm, the maintenance result is relatively stable, and the stability is higher than that of the traditional genetic algorithm;
(3) when the iteration times of each algorithm are changed, the operation result of the improved genetic algorithm is still better than that of the traditional genetic algorithm and has higher stability;
(4) the system elastic recovery system based on the improved genetic algorithm combines a greedy degree model to generate a part of optimized initial population, so that the convergence rate of the algorithm is improved;
(5) the system elastic recovery system based on the improved genetic algorithm realizes better individual variation through the first variation in the group, the associated variation avoids the pure randomness of the variation, enhances the relevance and the purpose of the variation, and enables the algorithm to have better iteration and solution results.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a node location diagram of the present invention;
FIG. 3 is a graph of the evolution of the optimal importance of the present invention;
FIG. 4 is a maintenance strategy of the present invention with the highest mission importance under time and spare part constraints;
FIG. 5 is a comparison graph of the evolution curves of the optimal importance of the improved genetic algorithm and the traditional genetic algorithm of the present invention;
FIG. 6 is a schematic diagram of the first mutation operator.
Detailed Description
The technical solution of the present invention is further illustrated by the following examples.
For the purpose of enhancing understanding of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and examples, which are provided for illustration only and are not intended to limit the scope of the present invention.
Fig. 1 is a schematic flow chart of a system elastic recovery method based on an improved genetic algorithm. The method comprises the following steps:
(1) and initializing algorithm parameters, and encoding the group individuals into maintenance paths of the nodes. In the embodiment of the invention, the selected experimental data is from a certain ship aircraft carrier, the number N of nodes of equipment required to be maintained by a certain ship system is set to be 50, the 50 node positions are determined according to the actual positions of the equipment on the certain ship, and the specific positions are shown in fig. 2, namely, the horizontal coordinate range (0,295) and the vertical coordinate range (0, 75). The node task importance z is an integer between (1 and 6), the number c of required nodes is an integer between (1 and 3), the equipment node maintenance time h is between (150,450), the maintenance small group number s is 5, the average walking speed v of maintenance personnel is 3/2, the minimum maintenance node number minTour of each group is 3, the population size popSize is 80, and the iteration number numIter is 600;
(2) an initial population of size 80 was generated using a semi-random semi-greedy idea. Wherein, 40 individuals are generated randomly through randderm () and rand _ breaks () functions, and the latter 40 individuals are selectively generated by a greedy strategy, and the specific flow is as follows:
1) among the N damaged nodes, the first node repaired by each repair team is randomly generated.
2) The greedy model is set as follows:
wherein: f (X, Y) represents the priority of the node X to the node Y, degree (X) represents the task importance of the node X, distance (X, Y) represents the distance between the node X and the node Y, h (X) represents the time required by maintenance of the node X, and 70 is a preference coefficient obtained through a simulation experiment and used for generating reasonable greedy.
3) And selecting the next maintenance node of each group according to a greedy strategy, namely the next node is the node with the highest current priority. By analogy, a repair sequence for each subgroup, and thus a sequence for a single individual, can be obtained. The cycle was 40 times, resulting in 40 initial individuals added to the initial population.
(3) Under the consideration of limited time, limited spare parts and grouped maintenance, a fitness function based on task importance is constructed, the specific method is to add time constraint and spare part constraint in the form of grouped parallel maintenance, the time constraint is t <1800 (unit: s), the consideration of maintenance group is simultaneous maintenance, and the specific method is as follows:
ti＜1800(i＝1，2，3，4，5) (2)
wherein each maintenance team maintains time tiComprises the following steps:
spare parts are constrained as:
in the formula, componentiAIndicates the number of parts A, components, used in total for the node repaired in actual repair by the ith repair teamiBIndicates the number of parts B, components, used in total for the node repaired in actual repair by the ith repair teamiCDenotes the number of parts C used in total for nodes where the repair is completed in actual repair by the i-th repair team, m denotes an index of a start node corresponding to the repair team repair, n denotes an index of an end node corresponding to the repair team repair, DjAIndicates the number of parts A used by the jth node maintained by the corresponding maintenance group, DjBIndicates the number of parts B used by the jth node in the maintenance of the corresponding maintenance group, DjCAnd the number of the parts C used for maintaining the jth node by the corresponding maintenance group is shown.
The task importance fitness function formula is as follows:
in the formula, totaldegree represents the total task importance degree completed by all maintenance subgroups, s represents the number of maintenance subgroups, k represents the node index, and z representsi[pRoute(k)]Indicating the mission importance of the kth node in the ith repair team repair sequence.
Further, when t isiNot less than 1800(i ═ 1, 2, 3, 4, 5) or componentiA> 12 or componentiB> 12 or componentiCWhen the time is more than 12, the circulating body is jumped out, the total task importance of all maintenance groups for maintenance completion, the number of the remained parts for maintenance and the time for each group for maintenance are calculated,and recording each group of maintenance sequences, t when each group of maintenance startsiAre all reset to 0.
(4) Evaluating the adaptive value of each generation of population individuals according to the fitness function, and recording the optimal individuals of the current population and the adaptive values thereof;
(5) algebra iteration, namely judging whether the adaptive value of the current generation optimal individual is higher than the adaptive value corresponding to the historical optimal individual, if so, updating the historical optimal adaptive value and the historical optimal individual, and continuing the following steps; if not, directly continuing the following steps;
(6) maintaining elite individuals for a parent population, generating a child population by inverting, crossing and sliding between groups, and further, matching special genes to realize first variation in the groups and finally generating the child population, wherein the specific method for matching the special genes to realize the first variation in the groups is to match first nodes of maintenance sequences in the elite individuals of the parent with corresponding first nodes of child individuals generated by inverting, crossing and sliding between the groups, and if the matching is successful, exchanging the next maintenance node of the first nodes of the maintenance sequences of the individuals of the parent with the corresponding next maintenance node of the child individuals to generate the child individuals, as shown in fig. 6.
(7) Judging whether an algorithm ending condition is met, if so, continuing the following steps, otherwise, performing population iteration number +1, and skipping to implement the step (4);
(8) outputting an optimal adaptive value and a corresponding optimal algebra, and drawing an optimal individual maintenance sequence;
(9) and measuring the recovered system elasticity value. System elasticity is defined as the ratio of the recovery value to the loss value of the system performance, and can be measured by a quotient elasticity model:
R(t)＝Recovery(t)/Loss(td) (8)
through the steps, the system elasticity recovery method based on the improved genetic algorithm, the optimal task importance and the system elasticity are obtained.
Fig. 2 is a schematic diagram showing specific positions of 50 nodes, where the positions of 50 nodes are determined according to the actual positions of devices on a ship.
Fig. 3 is a graph showing an evolutionary graph of the optimal importance of each generation of the improved genetic algorithm population, and it can be seen that the convergence rate of the graph is fast, the optimal task importance is 127, and the optimal generation number is 575.
FIG. 4 shows a maintenance strategy of the improved genetic algorithm with the highest task importance under time and spare part constraints, wherein 5 lines with different thicknesses represent maintenance paths of 5 maintenance subgroups, the initial total system task importance of 50 equipment nodes is 170, the maintenance time is limited to not more than 30min after all the equipment nodes are failed, the number of parts of three types in each group is A, B, C, the total number of the three types of the parts is 12, the maintenance strategy based on the improved genetic algorithm maintains 29 nodes, the first group of the maintenance sequences is 49, 46, 40, 18, 15, 41, the second group of the maintenance sequences is 38, 48, 13, 10, 16, 14, 25, the third group of the maintenance sequences is 43, 44, 28, 4, 45, 12, the fourth group of the maintenance sequences is 50, 7, 39, 26, 23, the fifth group of the maintenance sequences is 24, 17, 42, 47, 29, and the total system task importance after maintenance is 127, the optimal generation is 575 th generations, and the maintenance time is 1782.1 s.
FIG. 5 is a graph showing a comparison of the evolution curves of the optimal task importance of the improved genetic algorithm and the existing genetic algorithm, where the existing genetic algorithm is characterized in that the initial population is random and only includes three variations of inversion, crossing, and sliding between groups, the dark curve in the graph is the evolution curve of the optimal task importance of the improved genetic algorithm, the light curve is the evolution curve of the optimal task importance of the existing genetic algorithm, obviously, the dark curve has a faster convergence rate than the light curve, the improved genetic algorithm achieves the optimal solution in the 575 th generation, the optimal task importance is 127, the existing genetic algorithm achieves the optimal solution in the 542 th generation, and the optimal task importance is 124.
In the present invention, the system performance is related to the task importance, and the system elasticity value under the maintenance of the improved genetic algorithm in fig. 5 is 0.75, while the system elasticity value under the maintenance of the existing genetic algorithm is 0.729, therefore, the system elasticity under the maintenance of the improved genetic algorithm is higher.
The optimal and worst solutions for both algorithms were calculated, each algorithm was run 20 times, and the mean and mean square error were calculated 20 times, with the results shown in the following table:
TABLE 1 comparison table of task importance stability of two algorithms
TABLE 2 comparison table of iteration number stability of two algorithms
As can be seen from Table 1, the second improved genetic algorithm has the highest average task importance and smaller mean square error, which indicates that the algorithm has better stability; compared with the traditional genetic algorithm, the second improved genetic algorithm has high highest task importance, and the optimal solution of the algorithm is superior to that of the traditional genetic algorithm. As can be seen from Table 2, the algebraic mean of the improved genetic algorithm of chapter II is much smaller than that of the conventional genetic algorithm, and it can be seen that the convergence of the improved genetic algorithm of chapter II is better.
TABLE 3 mean importance of different iterations
TABLE 4 variance of importance for different iterations
TABLE 5 algebraic mean of different iterations
It can be found from tables 3 to 5 that, in different iteration times, the task importance average, the task importance variance and the algebraic average of the improved genetic algorithm are always kept superior when the system maintenance path is optimized, which indicates that the improved genetic algorithm can not only obtain the optimal solution, but also has high convergence speed and good stability under different iteration times.
In summary, the invention provides a system elastic recovery method based on an improved genetic algorithm, which is a system elastic recovery method based on a fitness function constructed by time, spare part constraint and task importance, adopts a grouping form to maintain equipment failure nodes in parallel, and takes total task importance as a system performance measurement index. The improved system elasticity recovery method of the genetic algorithm is characterized in that an initial population is not generated randomly, but generated purposefully by a greedy strategy with a greedy degree model as a guide, and after the population is reversed, crossed and slid, the initial variation of genes in a group of a descendant population is realized according to the excellent characteristics of a parent population and the special genes of the descendant population, so that the convergence of the algorithm can be accelerated, and the optimal solution of the algorithm can be improved. The solution of the invention is better and more stable than the existing genetic algorithm, especially under the condition of different iteration times.
Claims (7)
1. A system elasticity recovery method based on improved genetic algorithm is characterized by comprising the following steps:
(1) initializing algorithm parameters, and encoding group individuals into maintenance paths of nodes;
(2) generating an initial population, wherein individuals of part of the initial population are randomly generated, and the rest of the initial population is generated according to a greedy strategy;
(3) constructing a fitness function based on task importance degree under the consideration of limited time, limited spare parts and grouped maintenance;
(4) evaluating the adaptive value of each generation of population individuals according to the fitness function, and recording the optimal individuals of the current population and the adaptive values thereof;
(5) algebra iteration, namely judging whether the adaptive value of the current generation optimal individual is higher than the adaptive value corresponding to the historical optimal individual, if so, updating the historical optimal adaptive value and the historical optimal individual, and continuing the following steps; if not, directly continuing the following steps;
(6) maintaining elite individuals for the parent population, and generating a child population through inversion, crossing and sliding among groups;
(7) matching the parent individuals and the offspring individuals with special genes to realize the first variation in the group, and finally generating an offspring population;
(8) judging whether an algorithm ending condition is met, if so, continuing the following steps, otherwise, performing population iteration number +1, and skipping to implement the step (4);
(9) outputting an optimal adaptive value and a corresponding optimal algebra, and drawing an optimal individual, namely an optimal maintenance path;
(10) and measuring the recovered system elasticity value.
2. The improved genetic algorithm-based systematic elastic recovery method according to claim 1, wherein: the initialization algorithm parameters mainly include: the method comprises the following steps of a node position matrix xy, a node number n, a node task importance z, node maintenance time h, the number c of nodes required to be maintained, a maintenance small group number s, the average walking speed v of maintenance personnel, a minimum maintenance node number minTour, a population size popSize and an iteration number numIter.
3. The improved genetic algorithm-based systematic elastic recovery method according to claim 1, wherein the specific method for generating part of the initial population according to the greedy strategy in step (2) is as follows:
(a) randomly generating a first node maintained by each maintenance group in the N damaged nodes;
(b) setting a greedy degree model, wherein the formula is as follows:
f (X, Y) represents the greedy degree of the node X to the node Y, degree (X) represents the task importance degree of the node X, distance (X, Y) represents the distance between the node X and the node Y, h (X) represents the time required by maintenance of the node X, and 70 is a coefficient obtained through a simulation experiment and used for generating reasonable greedy degree;
(c) and selecting the next maintenance node of each group according to a greedy strategy, so as to obtain the maintenance sequence of each group and further obtain the sequence of a single individual, and circulating for M times to generate M initial individuals to be added into the initial population, wherein M is smaller than the size of the population.
4. The improved genetic algorithm-based systematic elastic recovery method according to claim 1, wherein the specific method for generating the final offspring population in step (7) is as follows: and (4) matching the filial generation population generated in the step (6) with a special gene to realize the first variation in the group, and finally generating the filial generation population.
5. The improved genetic algorithm-based systematic elastic recovery method according to claim 4, wherein the specific method for matching the variation in the specific gene realization group is as follows: and (4) matching each maintenance sequence first node in the parent elite individual with the corresponding first node of the offspring individual generated in the step (6), and if the matching is successful, taking the next maintenance node of the parent individual maintenance sequence first node as a variation point to exchange with the next maintenance node corresponding to the offspring individual to generate the final offspring individual.
6. The improved genetic algorithm-based systematic elastic recovery method according to claim 5, wherein the parent elite individual is obtained by the following specific method: and (3) grouping the parent population, selecting the individuals with the highest fitness value in each group as the elite individuals in each group by the algorithm, carrying out variation on the elite individuals in the parent to generate filial individuals, and further generating the filial population.
7. A system elasticity recovery system based on improved genetic algorithm is characterized in that: the method comprises a node initialization module, a population initialization module, a variation iteration module and an elasticity measurement module, wherein the node initialization module is used for initializing node positions, spare parts needed by nodes, node task importance, node maintenance time and minimum maintenance node number information, the population initialization module is used for generating an initial population, the initial population is generated by combining a random generation and greedy strategy method, the variation iteration module is used for inheriting parent elite individuals and generating offspring populations, the three variations and the first variation in the groups are included, when the maximum iteration times are reached, an algorithm is ended, and the elasticity measurement module is used for measuring the elasticity value of a system after recovery.
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| 周泽岩 等: "基于改进遗传算法的TSP问题求解的研究", 《技术与方法》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115225465A (en) * | 2022-07-18 | 2022-10-21 | 北京航空航天大学 | Network recovery method based on improved pigeon swarm algorithm |
| CN115225465B (en) * | 2022-07-18 | 2024-04-26 | 北京航空航天大学 | Network recovery method based on improved pigeon swarm algorithm |
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