CN114648232A - Cloud resource flexible job scheduling method based on improved chimpanzee optimization algorithm - Google Patents

Cloud resource flexible job scheduling method based on improved chimpanzee optimization algorithm Download PDF

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CN114648232A
CN114648232A CN202210318322.5A CN202210318322A CN114648232A CN 114648232 A CN114648232 A CN 114648232A CN 202210318322 A CN202210318322 A CN 202210318322A CN 114648232 A CN114648232 A CN 114648232A
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李少波
杨贵林
周鹏
蒲睿强
张黔富
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Abstract

The invention discloses a cloud resource flexible job scheduling method based on an improved chimpanzee optimization algorithm, which comprises the following steps: (1) adjusting a convergence factor of the chimpanzee algorithm and setting a position updating strategy of the chimpanzee algorithm; (2) and (3) carrying out scheme design of cloud-end resource scheduling according to the chimpanzee algorithm obtained in the step (1), and providing data reference. The invention can solve the problem of flexible job shop scheduling, and the invention provides a new GS, LS and random search combined initialization method, which improves the quality of population initial solution and accelerates the convergence speed of genetic algorithm. Compared with the test results of other genetic algorithms in the prior art, the calculation result is further improved, the calculation time is shortened to a certain extent, and the feasibility and the effectiveness of the provided initialization method are verified.

Description

Cloud resource flexible job scheduling method based on improved chimpanzee optimization algorithm
Technical Field
The invention belongs to the technical field of cloud-based resource flexible job scheduling, and relates to a cloud-based resource flexible job scheduling method based on an improved chimpanzee optimization algorithm.
Background
The cloud manufacturing related theory and research have problems to be studied deeply for the application in scheduling, which is a typical representative of the convergence of informatization and industrialization. On the basis of relevant research at home and abroad, the cloud resource scheduling problem facing the process in the cloud manufacturing environment is explored by combining a production flow. The flexible job shop scheduling problem of cloud resource modeling is an extension of the traditional shop scheduling problem, and for the traditional shop scheduling problem, the machining process of each workpiece and the machine and the machining time corresponding to each process are determined in advance. However, for the cloud-ended flexible workshop scheduling problem, the process involved in each workpiece can be processed on multiple machines, and the processing time of the selected machines is different under the premise. Compared with the traditional workshop scheduling method, the cloud resource scheduling problem increases the scheduling flexibility and relatively accords with the actual situation of actual production, so that the cloud resource scheduling problem is the problem which needs to be solved urgently at present.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the cloud resource flexible job scheduling method based on the improved chimpanzee optimization algorithm is provided to solve the technical problems in the prior art.
The technical scheme adopted by the invention is as follows: a cloud resource flexible job scheduling method based on an improved chimpanzee optimization algorithm comprises the following steps:
(1) adjusting the convergence factor of the chimpanzee algorithm and setting a position updating strategy of the chimpanzee algorithm;
(2) and (3) carrying out scheme design of cloud-end resource scheduling according to the chimpanzee algorithm obtained in the step (1), and providing data reference.
The adjustment of the convergence factor and the position updating strategy method are as follows:
s1-1, initial chimpanzee algorithm; the standard ChOA algorithm classifies chimpanzee populations into four types: the attacker, the handicapped, the driver and the chaser, wherein the attacker is the leader of the population, the other three classes of chimpanzees assist hunting, the social status is reduced in sequence, and the mathematical models of the chimpanzee expelling and chasing prey are as follows:
d＝|C·Xprey(t)-m·Xchimp(t)|
Xchimp(t+1)＝Xprey(t)-a·d
in the formula: t represents the current iteration number; xprey is a prey position vector; xchimp is the current chimpanzee position vector; a, m and C are coefficient vectors, and the calculation formula is as follows:
a＝2f·r1-f
m＝Chaotic value
C＝2·r2
in the formula: r1 and r2 are random vectors between [0, 1] respectively; f is a convergence factor, the value of which decreases nonlinearly from 2.5 to 0 as the number of iterations increases; a is a random vector for determining the distance between the chimpanzee and a hunting object, the value of the random vector is a random number between [ -f, f ], m is a chaotic mapping vector and represents the influence of a motivation in the hunting process of the chimpanzee; c is a control coefficient of chimpanzee expelling and hunting, and the value of the control coefficient is a random number between [0 and 2 ]; after the population is initialized, four optimal solutions are sequentially selected as the positions of an attacker, a barrier, a driver and a chaser, and the positions of other chimpanzees in the population are updated around the positions of the following four chimpanzees, wherein the mathematical model of the chimpanzees is described as follows:
X1＝Xattacker-a1·|C1·Xattacker-m1·X|
X2＝Xbarrier-a2·|C2·Xbarrier-m2·X|
X3＝Xchaser-a3·|C3·Xchaser-m3·X|
X4＝Xdriver-a4·|C4·Xdriver-m4·X|
X(t+1)＝(X1+X2+X3+X4)/4
s1-2, adjusting genetic factors of the chimpanzee algorithm, and accelerating the convergence rate of the algorithm:
Figure BDA0003569583140000031
where t is the current iteration number, MaxiterTo the maximum number of iterations, ainitialAnd afinalRespectively taking the initial value and the final value of a as 2 and 0;
s1-3, and meanwhile, in order to better balance the global search and local development process of the algorithm, a new self-adaptive step-shifting strategy is set:
Figure BDA0003569583140000032
s1-4, setting a dynamic proportional weight based on the guide position vector weight to enable the chimpanzee algorithm to be capable of efficiently optimizing;
distance weight between current chimpanzee individual to attacker, barrier, chaser, driver
Figure BDA0003569583140000033
Figure BDA0003569583140000034
Figure BDA0003569583140000035
Figure BDA0003569583140000036
In conjunction with the foregoing adaptive location update strategy, the final location update approach may be expressed as
Figure BDA0003569583140000037
The detailed method in the step (3) is as follows:
s3-1, simultaneously considering three performance indexes: the maximum completion time is minimum, the maximum load machine load is minimum and the total load on all machines is minimum;
maximum completion time CM
minCM＝min(max(CK)) 1≤k≤m
Where Ck is the machine MKThe completion time of (c);
maximum load machine load WM
minWM＝min(max(WK)) 1≤k≤m
In the formula, WKIs a machine MKThe workload of (2);
total load of all machines
Figure BDA0003569583140000041
S3-2, the LOV rule codes are more suitable for solving FJSP problem by chimpanzee algorithm, and a data structure is designed for a scheduling scheme according to the LOV rule:
s3-3, using SDCHOA to decode the data structure of step S3-2 to obtain a complete scheduling scheme.
The invention has the beneficial effects that: compared with the prior art, the invention can effectively combine the customer groups, scientifically and reasonably arrange the processing sequence of each workpiece and each procedure, realize the full utilization of processing resources and improve the processing efficiency of the workpieces. According to the principle of comprehensive operation balance, a standard model is applied to generate a workpiece processing sequence at a planning position; the following effects can be finally achieved:
1) establishing a data analysis method surrounding the key link, analyzing and researching the data of the key link, and establishing a standard model of distribution service;
2) the configuration of manufacturing (machine and personnel) resources is reasonable, the load capacity of the machine is reasonably increased, and the processing tasks of the machines are balanced;
3) and a task scheduling decision system is established to form regular data collection, analysis, application, improved flow specification and the like.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a graph showing a linear decreasing strategy with the parameter a and a proposed non-linear decreasing strategy.
Detailed Description
The invention is further described below with reference to specific examples.
Example 1: as shown in fig. 1-2, a cloud resource flexible job scheduling method based on an improved chimpanzee optimization algorithm includes the following steps:
(1) adjusting the convergence factor of the chimpanzee algorithm and setting a position updating strategy of the chimpanzee algorithm; the adjustment of the convergence factor and the position updating strategy method are as follows:
s1-1, initial chimpanzee algorithm; the standard ChOA algorithm classifies chimpanzee populations into four types: attacker, handicapped, expeller and chaser, wherein the attacker is the leader of the population, other three classes of chimpanzees assist the hunting, the social status declines in turn, and the mathematical models of chimpanzee expelling and chaser are as follows:
d＝|C·Xprey(t)-m·Xchimp(t)|
Xchimp(t+1)＝Xprey(t)-a·d
in the formula: t represents the current iteration number; xprey is a prey position vector; xchimp is the current chimpanzee position vector; a, m and C are coefficient vectors, and the calculation formula is as follows:
a＝2f·r1-f
m＝Chaotic value
C＝2·r2
in the formula: r1 and r2 are random vectors between [0, 1] respectively; f is a convergence factor, the value of which decreases nonlinearly from 2.5 to 0 as the number of iterations increases; a is a random vector for determining the distance between the chimpanzee and a hunting object, the value of the random vector is a random number between [ -f, f ], m is a chaotic mapping vector and represents the influence of a motivation in the hunting process of the chimpanzee; c is the control coefficient for chimpanzee expelling and catch-up prey, and the value is a random number between [0, 2 ]. After the population is initialized, four optimal solutions are sequentially selected as the positions of an attacker, a barrier, a driver and a chaser, and the positions of other chimpanzees in the population are updated around the positions of the following four chimpanzees, wherein the mathematical model of the chimpanzees is described as follows:
X1＝Xattacker-a1·|C1·Xattacker-m1·X|
X2＝Xbarrier-a2·|C2·Xbarrier-m2·X|
X3＝Xchaser-a3·|C3·Xchaser-m3·X|
X4＝Xdriver-a4·|C4·Xdriver-m4·X|
X(t+1)＝(X1+X2+X3+X4)/4
s1-2, adjusting genetic factors of the chimpanzee algorithm, and accelerating the convergence rate of the algorithm:
Figure BDA0003569583140000061
where t is the current iteration number, MaxiterTo the maximum number of iterations, ainitialAnd afinalRespectively taking the initial value and the final value of a as 2 and 0; figure 1 shows a curve comparison of the parameter a linear decreasing strategy with the proposed non-linear decreasing strategy;
as is clear from fig. 2, the nonlinear transition parameter is more focused on local development in more iterations than the original linear decreasing strategy. The values of the proposed non-linearity parameters are small in the middle and late stages of the iteration, which indicates that it facilitates local exploitation for a long time (about 62% of the maximum number of iterations) compared to global exploration. The figure also shows that the proposed non-linear parametric strategy only favors global exploration in about 38% of iterations during the search.
S1-3, in ChOA, initializing four groups of solutions of attacker, barrier, chaser and driver will be recorded and retained until the individuals with better adaptation value replace them in the iterative process. That is, if no better solution than recorded occurs in the population at the t-th generation, the current population still updates the location towards the four chimpanzees. However, when all four fall into local optima, it is difficult to find a better solution for the entire population at this time. It can be understood that: when a decision maker of a group of orangutan misjudges where a prey appears, the enclosing actions of all orangutan will be invalid and it will be difficult for them to find the prey in the wrong place.
The invention provides a new barrier, chaser and driver defining mode to strengthen the effect of the current generation of the optimal individual, thereby improving the global searching capability of the algorithm. In the algorithm implementation, as with ChOA, however, barrier, chaser, driver are defined as local variables that are fitness value optima in the tth generation and second, third chimpanzee individuals. Meanwhile, in order to better balance the global search and local development process of the algorithm, a new self-adaptive stepping strategy is provided, and the mathematical expression of the strategy is as follows:
Figure BDA0003569583140000071
s1-4, the disadvantage of the location update formula in ChOA is that X1、X2、X3、X4For this reason, two new proportional weight strategies, namely a weighted average strategy and a proportional weight strategy based on an adaptive value, are proposed in the prior art, and experimental verification is performed: some have proposed a dynamic proportional weight based on guiding the weight of the position vector; another person carries out analysis and experimental research on different weight strategies and theoretically proves the reason why the dynamic weight strategy can be used for high-efficiency optimization.
Distance weight between current chimpanzee individual to attacker, barrier, chaser, driver:
Figure BDA0003569583140000072
Figure BDA0003569583140000073
Figure BDA0003569583140000074
Figure BDA0003569583140000075
in practical applications, the above formulas are likely to have a denominator of 0. Therefore, a very small constant ε of 10 is added-16. They are modified into
Figure BDA0003569583140000081
Figure BDA0003569583140000082
Figure BDA0003569583140000083
Figure BDA0003569583140000084
A dynamic proportional weight based on a guide position vector weight is set, so that the chimpanzee algorithm can be efficiently optimized: in conjunction with the foregoing adaptive location update strategy, the final location update approach may be expressed as
Figure BDA0003569583140000085
(2) Comparing the test functions of the new chimpanzee algorithm obtained in the step (1) with other optimization algorithms;
other optimization algorithms are shown in the following table,
TABLE 1 benchmark test function
Figure BDA0003569583140000086
Table 1 shows the basic information for 10 benchmark test functions, of which 5 unimodal functions F1-F5, 3 nonlinear multimodal functions F6-F8 and 2 fixed-dimension multimodal functions F9-F10. Table 2 sets the parameters for each comparison algorithm;
(3) carrying out scheme design of cloud-end resource scheduling according to the chimpanzee algorithm obtained in the step (1), and providing data reference; the detailed method in the step (3) is as follows:
s3-1, simultaneously considering three performance indexes: minimum maximum completion time, minimum maximum load machine load and minimum total load on all machines;
maximum time-out CM
minCM＝min(max(CK)) 1≤k≤m
Where Ck is the machine MKThe completion time of (c);
maximum load machine load WM
minWM＝min(max(WK)) 1≤k≤m
In the formula, WKIs a machine MKThe workload of (2);
total load of all machines
Figure BDA0003569583140000091
S3-2, the LOV rule is more suitable for solving FJSP problem by chimpanzee algorithm. Designing a data structure for the design according to the LOV rules:
s3-3, decoding the data structure using SDChOA to obtain a complete scheduling scheme.
The effectiveness and superiority of SDCHOA are fully verified by the invention, and the SDCHOA is compared with goblet chiffon algorithm (SSA), Grey wolf algorithm (GWO), basic chimpanzee algorithm (ChOA) and chimpanzee algorithm (ChOA _ Levi) improved by Levy flight. The population size N is 30, and the spatial dimension dim is 10\30\50 iteration times tmax is 1000. It can be seen from table 2 that SDChOA has better optimizing effect than others in most functions.
TABLE 2 Algorithm test results
Figure BDA0003569583140000101
The specific application example is as follows: problem description and model construction: the flexible job shop scheduling problem is described as follows: n workpieces { J1, … …, Jn } are to be machined on M machines { M1, … …, Mm }. Each workpiece comprises one or more working procedures, the sequence of the working procedures is predetermined, each working procedure can be processed on a plurality of different processing machines, and the processing time of the working procedure is different according to the different processing machines. The dispatching objective is to select the most suitable machine for each process, determine the optimal processing sequence and the start time of each workpiece process on each machine, and optimize certain performance indexes of the whole system. Thus, the flexible job shop scheduling problem contains two sub-problems: determining the processing machine of each workpiece and determining the processing sequence on each machine. The scheduling that each process can be processed on any optional processing machine is called fully flexible job shop scheduling; conversely, scheduling where each process can only be processed on a portion of the alternative processing machines is referred to as partial flexible job shop scheduling, as shown in table 3.
TABLE 3 example of a part Flexible job shop scheduling problem
Figure BDA0003569583140000111
Note: j. the design is a square1Denotes the work 1, O12The 2 nd step of the 1 st workAnd the rest is similar.
In addition, the following constraints need to be satisfied during the machining process.
(1) Only one workpiece can be processed at a time on the same machine.
(2) The same process for the same workpiece can be processed by only one machine at the same time.
(3) Each process for each workpiece cannot be interrupted once processing is initiated.
(4) Different workpieces have the same priority.
(5) The procedures of different workpieces are not sequentially constrained, and the procedures of the same workpiece are sequentially constrained.
(6) All workpieces can be machined at time zero.
Three performance indexes are considered simultaneously: the maximum completion time is minimum, the maximum load machine load is minimum and the total load on all the machines is minimum, and the objective functions of the three performance indexes are respectively as follows.
(1) Maximum time-out CM
minCM＝min(max(Ck)) 1≤k≤m
In the formula, CkIs a machine MkThe completion time of (1).
(2) Maximum load machine load WM
minWM＝min(max(Wk)) 1≤k≤m
In the formula, WkIs a machine MkThe workload of (a).
(3) Total load WT of all machines
Figure BDA0003569583140000121
Table 1 is a process machine and process schedule for a flexible job shop scheduling problem including 2 workpieces, 5 machines. Here, "-" indicates that the corresponding machine cannot be selected for processing in this step. The problem listed in table 2 is a partially flexible job shop scheduling problem, and if all "-" in table 1 correspond to processing time, it indicates that each process for each workpiece can be selectively processed for all machines, which is a fully flexible job shop scheduling problem.
The method can solve the problem of flexible job shop scheduling, and the new GS, LS and random search combined initialization method provided by the invention can improve the quality of population initial solution and accelerate the convergence speed of genetic algorithm. Compared with the test results of other genetic algorithms in the prior art, the calculation result is further improved, the calculation time is shortened to a certain extent, and the feasibility and the effectiveness of the provided initialization method are verified.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and therefore the scope of the present invention shall be subject to the protection scope of the claims.

Claims (3)

1. A cloud resource flexible job scheduling method based on an improved chimpanzee optimization algorithm is characterized by comprising the following steps: the method comprises the following steps:
(1) adjusting the convergence factor of the chimpanzee algorithm and setting a position updating strategy of the chimpanzee algorithm;
(2) and (2) carrying out scheme design of cloud-end resource scheduling according to the chimpanzee algorithm obtained in the step (1), and providing data reference.
2. The cloud-based resource flexible job scheduling method based on the improved chimpanzee optimization algorithm according to claim 1, wherein the cloud-based resource flexible job scheduling method comprises the following steps: the method for adjusting the convergence factor and updating the position strategy is as follows:
s1-1, initial chimpanzee algorithm; the standard ChOA algorithm classifies chimpanzee populations into four types: attacker, handicapped, expeller and chaser, wherein the attacker is the leader of the population, other three classes of chimpanzees assist the hunting, the social status declines in turn, and the mathematical models of chimpanzee expelling and chaser are as follows:
d＝|C·Xprey(t)-m·Xchimp(t)|
Xchimp(t+1)＝Xprey(t)-a·d
in the formula: t represents the current iteration number; xprey is a prey position vector; xchimp is the current chimpanzee position vector; a, m and C are coefficient vectors, and the calculation formula is as follows:
a＝2f·r1-f
m＝Chaotic value
C＝2·r2
in the formula: r1 and r2 are random vectors between [0, 1], respectively; f is a convergence factor, the value of which decreases nonlinearly from 2.5 to 0 as the number of iterations increases; a is a random vector for determining the distance between the chimpanzee and a hunting object, the value of the random vector is a random number between [ -f, f ], m is a chaotic mapping vector and represents the influence of a motivation in the hunting process of the chimpanzee; c is a control coefficient of the chimpanzee expelling and chasing prey, the value of the control coefficient is a random number between [0 and 2], after the population is initialized, four optimal solutions are sequentially selected as the positions of an attacker, an obstacle, an expeller and a chaser, and the positions of other chimpanzees in the population are updated around the positions of the following four chimpanzees, and the mathematical model of the method is described as follows:
X1＝Xattacker-a1·|C1·Xattacker-m1·X|
X2＝Xbarrier-a2·|C2·Xbarrier-m2·X|
X3＝Xchaser-a3·|C3·Xchaser-m3·X|
X4＝Xdriver-a4·|C4·Xdriver-m4·X|
X(t+1)＝(X1+X2+X3+X4)/4
s1-2, adjusting genetic factors of the chimpanzee algorithm:
Figure FDA0003569583130000021
where t is the current iteration number, MaxiterTo the maximum number of iterations, ainitialAnd afinalRespectively an initial value and a final value of a, inThe values are 2 and 0 respectively;
s1-3, setting a new self-adaptive step-shifting strategy:
Figure FDA0003569583130000022
s1-4, setting a dynamic proportional weight based on the guide position vector weight;
distance weight between current chimpanzee individual to attacker, barrier, chaser, driver
Figure FDA0003569583130000023
Figure FDA0003569583130000024
Figure FDA0003569583130000025
Figure FDA0003569583130000026
In conjunction with the foregoing adaptive location update strategy, the final location update approach is represented as
Figure FDA0003569583130000027
3. The cloud-based resource flexible job scheduling method based on the improved chimpanzee optimization algorithm according to claim 1, wherein the cloud-based resource flexible job scheduling method comprises the following steps: the detailed method in the step (3) is as follows:
s3-1, simultaneously considering three performance indexes: the maximum completion time is minimum, the maximum load machine load is minimum and the total load on all machines is minimum;
maximum time-out CM
minCM＝min(max(CK))1≤k≤m
Where Ck is the machine MKThe completion time of (c);
maximum load machine load WM
minWM＝min(max(WK))1≤k≤m
In the formula, WKIs a machine MKThe workload of (a);
total load of all machines
Figure FDA0003569583130000031
S3-2, designing a data structure for the scheduling scheme according to the LOV rule:
s3-3, using SDCHOA to decode the data structure of step S3-2 to obtain a complete scheduling scheme.
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CN116233866A (en) * 2023-05-09 2023-06-06 北京智芯微电子科技有限公司 Method and system for optimizing distribution control of wireless sensor
CN116684960A (en) * 2023-06-15 2023-09-01 哈尔滨工程大学 Anchor node optimization method based on improved gray wolf optimization algorithm

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116233866A (en) * 2023-05-09 2023-06-06 北京智芯微电子科技有限公司 Method and system for optimizing distribution control of wireless sensor
CN116233866B (en) * 2023-05-09 2023-08-04 北京智芯微电子科技有限公司 Method and system for optimizing distribution control of wireless sensor
CN116684960A (en) * 2023-06-15 2023-09-01 哈尔滨工程大学 Anchor node optimization method based on improved gray wolf optimization algorithm

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