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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- chimpanzee
- algorithm
- cloud
- flexible job
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 241000282577 Pan troglodytes Species 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000005457 optimization Methods 0.000 title claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 230000002068 genetic effect Effects 0.000 claims abstract description 7
- 238000013461 design Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 25
- 230000008569 process Effects 0.000 claims description 17
- 241000282579 Pan Species 0.000 claims description 12
- 230000004888 barrier function Effects 0.000 claims description 8
- 230000000739 chaotic effect Effects 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 6
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 3
- 230000007423 decrease Effects 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 230000008450 motivation Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 abstract description 6
- 238000011423 initialization method Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 description 17
- 230000006870 function Effects 0.000 description 8
- 230000003247 decreasing effect Effects 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000003754 machining Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 241000282405 Pongo abelii Species 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000005477 standard model Effects 0.000 description 2
- 241000282461 Canis lupus Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06316—Sequencing of tasks or work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Evolutionary Computation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Educational Administration (AREA)
- Artificial Intelligence (AREA)
- Development Economics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Manufacturing & Machinery (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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
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:
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:
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
In conjunction with the foregoing adaptive location update strategy, the final location update approach may be expressed as
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
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:
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:
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:
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
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
(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
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
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
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
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
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:
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:
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
In conjunction with the foregoing adaptive location update strategy, the final location update approach is represented as
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
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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210318322.5A CN114648232A (en) | 2022-03-29 | 2022-03-29 | Cloud resource flexible job scheduling method based on improved chimpanzee optimization algorithm |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210318322.5A CN114648232A (en) | 2022-03-29 | 2022-03-29 | Cloud resource flexible job scheduling method based on improved chimpanzee optimization algorithm |
Publications (1)
Publication Number | Publication Date |
---|---|
CN114648232A true CN114648232A (en) | 2022-06-21 |
Family
ID=81994687
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210318322.5A Pending CN114648232A (en) | 2022-03-29 | 2022-03-29 | Cloud resource flexible job scheduling method based on improved chimpanzee optimization algorithm |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114648232A (en) |
Cited By (2)
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 |
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 |
-
2022
- 2022-03-29 CN CN202210318322.5A patent/CN114648232A/en active Pending
Cited By (3)
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 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109886589B (en) | Method for solving low-carbon workshop scheduling based on improved whale optimization algorithm | |
CN114648232A (en) | Cloud resource flexible job scheduling method based on improved chimpanzee optimization algorithm | |
Lo et al. | A genetic algorithm with new local operators for multiple traveling salesman problems | |
CN113792924A (en) | Single-piece job shop scheduling method based on Deep reinforcement learning of Deep Q-network | |
CN113011612B (en) | Production and maintenance scheduling method and system based on improved wolf algorithm | |
CN106611231A (en) | Hybrid particle swarm tabu search algorithm for solving job-shop scheduling problem | |
CN111813500B (en) | Multi-target cloud workflow scheduling method and device | |
CN109917754B (en) | Multi-population distribution estimation algorithm-based robot assembly unit multi-target layout optimization method | |
CN112348314A (en) | Distributed flexible workshop scheduling method and system with crane | |
CN116700176A (en) | Distributed blocking flow shop scheduling optimization system based on reinforcement learning | |
Fiasché et al. | Human-centric factories 4.0: A mathematical model for job allocation | |
CN113901728B (en) | Computer second-class assembly line balance optimization method based on migration genetic algorithm | |
CN116880424A (en) | Multi-robot scheduling method and device based on multi-objective optimization | |
CN110490446A (en) | A kind of modular process recombination method based on improved adaptive GA-IAGA | |
CN116985146B (en) | Robot parallel disassembly planning method for retired electronic products | |
CN112528524A (en) | Balanced and optimized scheduling method for mixed-flow assembly line | |
CN110362378A (en) | A kind of method for scheduling task and equipment | |
CN112700109A (en) | Order production sequence optimization method, system, equipment and readable storage medium | |
CN115169754B (en) | Energy scheduling method and device, electronic equipment and storage medium | |
Elsayed et al. | Parameters adaptation in differential evolution | |
CN115545329A (en) | Integrated dynamic scheduling method suitable for clothing manufacturing production line | |
CN116090773A (en) | Flexible job shop scheduling method based on improved wolf algorithm | |
Liu et al. | An improved nondominated sorting genetic algorithm-ii for multi-objective flexible job-shop scheduling problem considering worker assignments | |
Poohoi et al. | A novel crossover operator for genetic algorithm: Stas crossover | |
Nappu et al. | Path-relinking Grey Wolf Optimizer for Solving Operation Sequencing Problem |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |