CN111191352B - System elastic recovery algorithm considering time and task importance - Google Patents
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Abstract
The invention discloses a system elastic recovery algorithm considering time and task importance, which comprises the steps of initializing parameter values of a population, coding individuals in the population into a maintenance sequence, adding information such as the importance and maintenance time into each node, adding time constraint, constructing an adaptive value function based on the task importance, obtaining the node importance of each group of maintenance personnel which can be maintained in limited time, and obtaining the total importance of the individuals through accumulation. The method searches and obtains the optimal individual with the highest total importance in the population, and then obtains the optimal individual in all algebras through iteration of the genetic algorithm, namely the optimal maintenance sequence. The advantages are that: the system elasticity recovery algorithm considering time and task importance is simple and efficient, the maintenance strategy and the maintenance result which enable the task importance to be the highest in limited time can be rapidly obtained, and the recovery algorithm can enable the damaged system elasticity to be rapidly recovered.
Description
Technical Field
The invention relates to the technical field of algorithm application, in particular to a system elastic recovery algorithm considering time and task importance.
Background
The term elasticity originally derives from the latin phrase "resiliere", meaning "rebound". Common definitions of the term elastic mean the ability of an entity or system to return to a normal state after an event occurs. The elastic opinion originated in ecology in the early 60, 70's of the 20 th century, and was mainly focused on the interaction of populations such as predators and prey and their related research on the theory of ecological stability. With the increasing emphasis on elasticity, elasticity is constantly being used and developed in life, and the elasticity viewpoint is now applicable to various fields such as ecology, material science, engineering, psychology and economics.
Elasticity is a term that refers to the ability of a tissue or system to respond or rebound when encountering an unexpected risk. Resiliency is a method of diverting the system from unexpected or damaging events to an adaptation to ensure that the system maintains its continuity of operation over a period of time. System flexibility reflects the ability of the system to absorb shock and recover, while being a means of operation to change its structure and face long-term stress, variations and uncertainties.
From a system perspective, resiliency has been seen as an attribute of a system, namely the ability of a system to recover its basic (or general) functionality after being attacked (or disturbed) resulting in physical damage to the system and going beyond its control range. The establishment of the corresponding maintenance strategy and to what extent it can be restored is particularly important for the flexibility of the system.
For the situation, applying the intelligent optimization algorithm to the recovery of the system elasticity is a better solution. Genetic Algorithm (GA) used herein is a search algorithm established by simulating the Genetic and evolutionary processes in the biological world, and embodies the competition mechanism of "competition for survival, high-quality and low-quality survival of suitable persons". The genetic algorithm has good global search capability, can quickly search out the whole solution in the solution space without trapping in a quick descending trap of a local optimal solution, and can conveniently perform distributed calculation by utilizing the intrinsic parallelism of the genetic algorithm to accelerate the solving speed.
The setting of the adaptive value function of the existing intelligent optimization algorithm rarely relates to information such as task importance, so that how many tasks of a repaired system can be completed in a normal state is difficult to measure, and the consideration of time constraints and grouping modes is rare, which are aspects to be improved.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the background art, the invention discloses a system elastic recovery algorithm considering time and task importance, which can make a corresponding maintenance strategy under the condition of considering time and task importance, so that a system damaged by attack or interference can recover the basic or general functions, the elastic capability of the system is displayed, and the anti-attack capability and the adaptive capability of the system are improved.
The technical scheme is as follows: the invention relates to a system elastic recovery algorithm considering time and task importance, which comprises the following steps:
(1) initializing all parameters of a population, coding individuals into a maintenance sequence, and constructing an adaptive value function based on task importance under the condition of considering limited time;
(2) evaluating the current adaptive value of each individual in the population according to the adaptive value function of the importance degree, and searching out the individual with the highest importance degree in the current population;
(3) judging whether the importance of the optimal individuals of the population is higher than that of the optimal individuals of all previous generations, if so, continuing the following steps, if not, updating the individuals in the population according to the genetic algorithm, adding 1 to the population algebra, and skipping to implement the step (2) (namely, continuing to execute the genetic algorithm);
(4) replacing the historical optimal individual with the optimal individual;
(5) judging whether an algorithm ending condition is met, if so, continuing the following steps, if not, updating individuals in the population according to a genetic algorithm, adding 1 to the population algebra, and skipping to implement the step (2);
(6) outputting a global optimal adaptation value totalMetr and an optimal algebra iter;
(7) the elasticity of the system is measured.
The system elastic recovery algorithm considering time and task importance is simple and efficient, can quickly acquire the maintenance strategy and the maintenance result which enable the task importance to be the highest in limited time, and can quickly recover the damaged system performance through the elastic performance.
The specific method of considering the limited time in the step (1) is to add importance and maintenance time information into each node, and add time constraint and a form of group maintenance.
Further, in the step (1), the number of nodes is 40, the abscissa and the ordinate are random numbers between (0,100), the importance of the nodes is random numbers between (1,5), the maintenance time of the nodes is random numbers between (1,500), the total initialization time t is 0, the population size popSize is 80, and the maximum number of iterations numiters is 1000; the total time constraint t <1800 is added and the number of packets nSalesmen is 5.
Wherein the expression of the total time constraint t is:
t＝t+h(pRoute(k))+[dmat(pRoute(k),pRoute(k+1))]v, maintenance time expression t for each maintenance groupi(i ═ 1,2,3,4,5) is:
in the formula, k represents an index of a node, m represents an index of a start node corresponding to maintenance of the maintenance team, n represents an index of an end node corresponding to maintenance of the maintenance team, h (pRoute (k)) represents time required for a maintenance worker to maintain the kth node, dmat (pRoute (k), pRoute (k +1)) represents a distance from the kth node to the kth +1 node in a maintenance route, and v represents a walking speed of the maintenance worker.
Total time constraint of t<1800, time constraint t because the repair team is repairing at the same timei(i ═ 1,2,3,4,5) < 1800. When t isiWhen (i is 1,2,3,4,5) > (1800), the loop body is skipped, and the calculated total task importance is the sum of the importance of the nodes after the maintenance is completed. The fitness function totaltmer is then the sum of the task importance of each maintenance team. And evaluating the current adaptive value of each individual in the population according to the adaptive value function.
The task importance adaptive value function is as follows:
wherein totalMetr represents the total task importance, z (pRoute (k)) represents the task importance of the kth node in the maintenance route, and metriIndicating the overall task importance of the i-th maintenance group, k indicating the index of the node, m indicating the index of the start node of the maintenance of the corresponding maintenance group, n' indicating the capability of the corresponding maintenance group in the constraint timeThe index of the last node that is completed is repaired.
Further, the step (7) is as follows: the elasticity value of the system is measured according to the quotient elasticity model.
Has the advantages that: compared with the prior art, the invention has the advantages that: (1) the system elastic recovery algorithm considering time and task importance is simple and efficient, the maintenance strategy and the maintenance result which enable the task importance to be the highest in limited time can be rapidly obtained, and the elastic performance can enable the performance of the damaged system to be rapidly recovered; (2) a new adaptive value function based on task importance is adopted, and the optimal task importance which can be achieved by system maintenance in the specified time can be effectively obtained on the basis of time constraint; (3) the genetic algorithm is used for algorithm application, has good global search performance and can effectively and quickly perform global search in a probabilistic sense; (4) time constraint is added in the algorithm, so that the time constraint condition of the wartime situation is effectively simulated, and the optimal degree and the optimal maintenance strategy which can be reached by system maintenance in the specified time can be obtained; (5) the algorithm adds a group maintenance strategy, which can obtain a better maintenance strategy and higher task importance than a single group of maintenance.
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 a node distance matrix of the present invention;
FIG. 4 is a graph of the optimal importance evolution of the present invention;
FIG. 5 is a diagram of the maintenance strategy of the present invention for the highest mission importance under time constraints;
FIG. 6 is a maintenance strategy diagram for random maintenance under time constraints in accordance with the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The system elastic recovery algorithm considering time and task importance as shown in fig. 1 comprises the following steps:
(1) the individual codes of the population are the maintenance sequence, the parameters of the population are initialized, and the information such as the importance degree and the maintenance time is added. The number of nodes is 40, the abscissa and the ordinate are random numbers between (0,100), the importance of the nodes is random numbers between (1,5), the maintenance time of the nodes is random numbers between (1,500), the total initialization time t is 0, the population size popSize is 80, and the maximum iteration number numIter is 1000;
and adding a time constraint and a grouping maintenance form to construct an adaptive value function based on the task importance. Maintenance time expression t for each maintenance groupi(i ═ 1,2,3,4,5) is:
wherein k represents an index of a node, m represents an index of a start node corresponding to maintenance of the maintenance team, n represents an index of an end node corresponding to maintenance of the maintenance team, h (pRoute (k)) represents time required for a maintenance worker to maintain the kth node, dmat (pRoute (k), pRoute (k +1)) represents a distance from the kth node to the kth node in a maintenance route, and v represents a walking speed of the maintenance worker;
total time constraint of t<1800, time constraint t because the repair team is repairing at the same timei(i＝1,2,3,4,5)＜1800。
The form of group maintenance is adopted, the maintenance group number nSalesmen is 5, namely the maintenance is divided into 5 maintenance groups to be simultaneously maintained, and at least the maintenance node number minTour in each group is 3.
Constructing an adaptive value function based on the task importance degree:
wherein k represents a cord of a nodeIn the following description, m denotes an index of a start node to be repaired by a corresponding repair team, n' denotes an index of a last node to be repaired by the corresponding repair team within a constraint time, and metriIndicating the overall mission importance of the ith maintenance team.
The time constraint in the formula is ti(i ═ 1,2,3,4,5) <1800, when t isiWhen (i is 1,2,3,4,5) > (1800), the loop body is skipped, and the calculated total task importance is the sum of the importance of the nodes after the maintenance is completed. The fitness function totaltmer is then the sum of the task importance of each maintenance team.
(2) Evaluating the current adaptive value of each individual in the population according to the adaptive value function of the importance degree, traversing all the individuals in the population, and searching out the individual with the highest importance degree in the current population;
wherein index represents the index of the task importance totaltmer, iter represents the population generation number, popgross represents the population maintenance route, optallate represents the individual maintenance route, poppeak represents the population separation matrix, and optBreak represents the individual separation matrix, which is used to separate the maintenance tasks of the maintenance team.
And searching to obtain the optimal individual in the current algebra, thereby obtaining the optimal importance totaltMetr.
(3) Judging whether the importance of the optimal individuals of the population is higher than that of the optimal individuals of all previous generations, if so, continuing the following steps, and if not, updating the individuals in the population according to the genetic algorithm, adding 1 to the population algebra, and continuing to execute the genetic algorithm (step (2));
(4) replacing the historical optimal individual with the optimal individual;
wherein iter represents the population generation number, popource represents the population maintenance route, optresote represents the individual maintenance route, poppeak represents the population separation matrix, and optBreak represents the individual separation matrix.
(5) And (2) judging whether the algorithm ending condition is met, if so (namely the iteration number numIter is 1000), continuing the following steps, and if not, updating the individuals in the population according to the genetic algorithm, adding 1 to the population generation number, and continuing the execution of the genetic algorithm (step (2)).
(6) And outputting the global optimal adaptation value totaltMetr and the optimal algebraic iter, obtaining a maintenance strategy of optimal task importance, showing the elastic recovery capability of the system, and measuring the elastic value of the system.
(7) The elasticity value of the system is measured according to the quotient elasticity model.
Through the steps, the maintenance strategy of the system elastic recovery considering the time and the task importance are obtained.
As shown in fig. 2, which is a schematic diagram of specific positions of 40 nodes, the positions of 40 nodes are all randomly generated.
As shown in fig. 3, the distance matrix of the nodes is obtained by converting the values of the elements in the distance matrix into different colors according to the size by using the imagesc function, and dyeing the values with the colors at the corresponding positions of the coordinate axes, wherein the darker the color represents the smaller the value.
As shown in fig. 4, which is an iterative evolution curve of the optimal importance of each generation of the population, it can be seen that the curve converges quickly and then flattens, the optimal importance is 94.4, and the optimal generation is 671 generations.
Fig. 5 shows the maintenance strategy with the highest task importance under the time constraint, and fig. 6 shows the maintenance strategy with random maintenance under the time constraint. In both fig. 5 and 6 there are 5 broken line bars representing the repair path of 5 repair teams. The total task importance of the 40 nodes is 95.9, the time constraint is set to be 30 minutes, the total number of nodes which can be repaired based on the maintenance strategy with the highest task importance is 30, the total task importance is 94.4, and the optimal generation number is 671 generations. In the maintenance strategy of random maintenance, the total number of nodes which can be repaired is 29, but the total task importance is only 76.3.
System resiliency is defined as the ratio of the recovery value to the loss value of a performance, i.e. the resilience of a system is measured by the ratio of the recovered performance level to the reduced performance level:
R(t)＝Recovery(t)/Loss(td)，
in the formula: r (t) is the system elasticity at time t, recovery (t) is the system performance recovered at time t, Loss (t)d) Is the loss value of the system performance. This formula demonstrates the ability of the system to return to the original performance level, i.e., the ability to recover from a jamming event. If the system can be restored to the initial state, i.e. recovery (t) ═ Loss (t)d) Then the system is fully elastic; if there is no recovery, i.e. recovery (t) is 0, the system exhibits no resilience. This elasticity equation is derived from the ratio of the properties and is therefore called the quotient elasticity model.
Here, the system performance index is measured by the task importance. From the quotient elastic model, the system elasticity value is 0.984 in the maintenance strategy shown in fig. 5 with the highest task importance under the time constraint, while the system elasticity value is 0.796 in the maintenance strategy shown in fig. 6 with random maintenance under the time constraint. The comparison shows that the system shown in fig. 5 has better elasticity.
The system elastic recovery algorithm considering time and task importance provided by the invention measures the optimal maintenance strategy based on the adaptive value function constructed by time constraint and task importance. The method for system elastic recovery considering time and task importance is characterized in that the time factor is taken into consideration in the system elastic recovery, a maintenance process is carried out in a grouping mode, the excellence of a maintenance strategy is measured according to the size of a total task importance value, and the maintenance strategy is better when the task importance is higher. Adding information such as importance, maintenance time and the like into each node, adding time constraint and grouping strategy, constructing an adaptive value function based on task importance, measuring each individual in a population, searching to obtain an optimal individual with the highest total weight importance in the population, and obtaining the optimal individual in all algebras through iteration of a genetic algorithm, namely an optimal maintenance sequence. The system elastic recovery algorithm considering time and task importance is simple and efficient, the maintenance strategy and maintenance results which enable the task importance to be the highest in limited time can be rapidly obtained, and the system performance after damage can be rapidly recovered through the elastic recovery algorithm.
Claims (3)
1. A system resiliency recovery algorithm taking into account time and task importance, comprising the steps of:
(1) initializing all parameters of a population, coding individuals into a maintenance sequence, and constructing an adaptive value function based on task importance under the condition of considering limited time;
each code in the individual codes is a node, and the specific method considering the limited time is to add importance and maintenance time information into each node and add time constraint and a grouping maintenance form;
maintenance time expression t for each maintenance groupiWherein i ═ 1,2,3,4,5, is:
wherein k represents an index of a node, m represents an index of a start node corresponding to maintenance of the maintenance team, n represents an index of an end node corresponding to maintenance of the maintenance team, h (pRoute (k)) represents time required for a maintenance worker to maintain the kth node, dmat (pRoute (k), pRoute (k +1)) represents a distance from the kth node to the kth node in a maintenance route, and v represents a walking speed of the maintenance worker;
the task importance adaptive value function is as follows:
wherein, totalMetr tableIndicating the total task importance, z (pRoute (k)) indicating the task importance of the kth node in the maintenance route, and metriThe total task importance of the ith group of maintenance groups is represented, k represents the index of a node, m represents the index of a starting node corresponding to maintenance of the maintenance groups, and n' represents the index of the last node which can be maintained within the constraint time by the corresponding maintenance groups;
(2) evaluating the current adaptive value of each individual in the population according to the adaptive value function of the importance degree, and searching out the individual with the highest importance degree in the current population;
(3) judging whether the importance of the optimal individuals of the population is higher than that of the optimal individuals of all previous generations, if so, continuing the following steps, otherwise, updating the individuals in the population according to a genetic algorithm, adding 1 to the population algebra, and skipping to implement the step (2);
(4) replacing the historical optimal individual with the optimal individual;
(5) judging whether an algorithm ending condition is met, if so, continuing the following steps, if not, updating individuals in the population according to a genetic algorithm, adding 1 to the population algebra, and skipping to implement the step (2);
(6) outputting a global optimal adaptation value totalMetr and an optimal algebra iter;
(7) the elasticity of the system is measured.
2. The system elastic recovery algorithm considering time and task importance according to claim 1, wherein: in the step (1), the number of nodes is 40, the abscissa and the ordinate are random numbers between (0,100), the importance of the nodes is random numbers between (1,5), the maintenance time of the nodes is random numbers between (1,500), the total initialization time t is 0, the population size popSize is 80, and the maximum iteration number numIter is 1000; the total time constraint t <1800 is added and the number of packets nSalesmen is 5.
3. The system elastic recovery algorithm considering time and task importance according to claim 1, wherein the step (7) is: the elasticity value of the system is measured according to the quotient elasticity model.
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