CN111027665A - Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm - Google Patents

Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm Download PDF

Info

Publication number
CN111027665A
CN111027665A CN201911216364.2A CN201911216364A CN111027665A CN 111027665 A CN111027665 A CN 111027665A CN 201911216364 A CN201911216364 A CN 201911216364A CN 111027665 A CN111027665 A CN 111027665A
Authority
CN
China
Prior art keywords
bat
chaotic
improved
matrix
swarm algorithm
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
Application number
CN201911216364.2A
Other languages
Chinese (zh)
Inventor
简琤峰
陈家炜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN201911216364.2A priority Critical patent/CN111027665A/en
Publication of CN111027665A publication Critical patent/CN111027665A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/08—Computing arrangements based on specific mathematical models using chaos models or non-linear system models
    • 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
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • 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
    • 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)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • Data Mining & Analysis (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Primary Health Care (AREA)
  • Mathematical Analysis (AREA)
  • Biomedical Technology (AREA)
  • Manufacturing & Machinery (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Nonlinear Science (AREA)
  • Algebra (AREA)
  • Computational Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Molecular Biology (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to a cloud manufacturing scheduling method based on an improved chaotic bat swarm algorithm, which generates a matrix of a swarmPConfirming the population number, initializing the target configuration and dividing the task tomSubtask, preset virtual machine asnThe platform initializes the global optimum to obtain the optimum scheduling scheme, and outputs the final optimum scheduling scheme after using the improved chaos bat swarm algorithm to perform scheduling operationglobalbestAnd the cloud scheduling method is used as an optimal cloud manufacturing scheduling scheme. The invention is an improved algorithm based on bat group, which blends chaos into particle motion to make bat group move alternately between chaos and stability, and approaches to optimal point gradually, thereby improving calculation precision and further improving total efficiencyLocal optimization ability; the original algorithm is improved by combining two-dimensional disturbance and the chaotic factor, so that a better effect is achieved.

Description

Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm
Technical Field
The present invention relates to computing; calculating; the technical field of counting, in particular to a cloud manufacturing scheduling method based on an improved chaotic bat swarm algorithm for a computer system based on a specific computing model.
Background
In the operation process of the cloud manufacturing platform, a service demander continuously submits own manufacturing requirements to the cloud manufacturing platform, and the cloud manufacturing platform provides optimal manufacturing services for users and generates a task and service scheduling scheme based on the personalized requirements of each user.
The matching and scheduling process of the manufacturing tasks and the manufacturing services is the core of solving the supply and demand matching and the resource effective utilization of the cloud manufacturing platform, and the process needs to consider the optimization selection problem among different manufacturing services.
In the prior art, the research on scheduling problems of tasks and services of manufacturing resources in a cloud manufacturing environment is still less, the used method is mainly based on an intelligent optimization algorithm, such as a genetic algorithm, a peak swarm algorithm, a particle swarm algorithm, an ant colony algorithm and the like, and some game theory methods are applied to task scheduling problems in a networked manufacturing environment.
However, although the algorithm in the prior art can guarantee the distribution planning of tasks, the execution speed and the load balance degree of the scheduling scheme cannot be guaranteed, and the cloud manufacturing scheduling cannot be perfectly implemented finally.
Disclosure of Invention
The invention solves the problems in the prior art, provides an optimized cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm, and improves the original algorithm by combining two-dimensional disturbance and a chaotic factor so as to achieve better effect.
The invention adopts the technical scheme that a cloud manufacturing scheduling method based on an improved chaotic bat swarm algorithm comprises the following steps:
step 1: generating a population P, wherein P is a matrix and the number of the population is P;
step 2: initializing target configuration, dividing tasks into m subtasks, and presetting n virtual machines;
and step 3: initializing global optimization, obtaining an optimal solution bestfittness of an optimal value algorithm of the fitness of the scheduling scheme, a population row bestindex corresponding to the optimal solution, and an optimal scheduling scheme globalbest, wherein the globalbest is a vector with the length of m;
and 4, step 4: using an improved chaotic bat swarm algorithm to perform scheduling operation;
and 5: and outputting globalbest as an optimal cloud manufacturing scheduling scheme.
Preferably, in step 1, the number of columns of the matrix P is the number of the population, the number of rows is the number of the tasks, and the value of any element is the number of the virtual machine.
Preferably, in step 3, the scheduling scheme fitness includes run time and load balancing.
Preferably, the step 4 comprises the steps of:
step 4.1: initializing a matrix P, wherein each element in the matrix corresponds to a speed, and the speed matrix is V;
initializing loudness A corresponding to the population quantity p, average loudness Ampan corresponding to the population quantity p, frequency F, maximum element fmax and minimum element fmin of the frequency F, pulse emissivity r, search measure faid, proportion Mi of space search of bats moving towards a negative direction, chaotic factor rid and chaotic variable cid;
step 4.2: initializing data, wherein the loudness A is any value in [0,2], the pulse emissivity r is a uniformly distributed random number between [0,1], and the chaos factor rid is any value in [0,1 ]; let iteration number iter be 1, and maximum iteration number gmax;
step 4.3: if iter is less than gmax, then go to the next step, otherwise, go to step 5;
step 4.4: initializing chaotic variables cid, cid [ i]t＝(cid[i]t-1)1+rid[i]；
Updating the frequency F [ i ] of each bat to fmin + (fmax-fmin) multiplied by β;
updating speed information v [ i ] [ j ] - [ v [ i ] [ j ] + (pop [ i ] [ j ] -globalbest [ j ]) × F [ i ], wherein i is the ith row of P, t is an order identifier, j is the jth column of P, pop [ i ] [ j ] is an element of the jth row of the ith row in the matrix P, and β is a random variable subject to uniform distribution among [0 and 1 ];
step 4.5: calculation of pop [ i][j]t+1＝ω×v[i][j]t+1+(1-0.2×σ)×pop[i][j]tWherein ω and σ are both [0,1]]Random numbers obeying uniform distribution;
step 4.6: let pop [ i ] [ j ] ═ pop [ i ] [ j ] + epsilon × amaan, where epsilon is a random number between [ -1,1] subject to uniform distribution;
chaotic optimization is carried out on the result after the two-dimensional disturbance to obtain
Figure BDA0002299634250000031
Step 4.7: rounding and border crossing judgment are carried out on each element of the matrix P;
step 4.8, updating the frequency F [ i ] ═ fmin + (fmax-fmin) × β;
update loudness
Figure BDA0002299634250000032
Pulse emissivity ri]t+1＝r[i]t(1-e-γt)，
Figure BDA0002299634250000033
And gamma are both [0,1]]Random numbers obeying uniform distribution;
step 4.9: based on step 4.8, updating average loudness ocean, global optimum bestfittness, bestindex and globalbest, iter ═ iter +1, and returning to step 4.3.
Preferably, in said step 4.1, the initial value of each element in the velocity matrix V is 0.
Preferably, in said step 4.1, the search measure faid ═ m-1.
Preferably, in said step 4.1, the initial value of the proportion Mi of the space search of the bat moving in the negative direction is 0.
Preferably, in the step 4.1, the chaotic variable cid is epsilon [0,1 ].
The invention relates to an optimized cloud manufacturing scheduling method based on an improved chaotic bat swarm algorithm.
The invention is based on the improved algorithm on the basis of the bat group, the chaos is integrated into the particle motion, so that the bat group moves alternately between chaos and stability and gradually approaches to an optimal point, the calculation precision is improved, and the global optimization capability is further improved; the original algorithm is improved by combining two-dimensional disturbance and the chaotic factor, so that a better effect is achieved.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a comparison of the iterative trend of the algorithm when the number of tasks is 30 according to the embodiment of the present invention;
FIG. 3 is a comparison of the iterative trend of the algorithm when the number of tasks is 300 according to the embodiment of the present invention;
in fig. 2 and 3, the abscissa represents the number of runs and the ordinate represents the time overhead; in the embodiment, the node graph corresponding to the method is a square.
Detailed Description
The present invention is described in further detail with reference to the following examples, but the scope of the present invention is not limited thereto.
The invention relates to a cloud manufacturing scheduling method based on an improved chaotic bat swarm algorithm, which comprises the following steps.
Step 1: and generating a population P, wherein P is a matrix and the number of the population is P.
In step 1, the number of columns of the matrix P is the number of the population, the number of rows is the number of the tasks, and the value of any element is the number of the virtual machine.
In the invention, the number of rows of the population P is the number of the population, each row represents a scheduling scheme, the number of columns is the number of the virtual machines, and the number of each element represents the number of the task allocated to the virtual machine.
Step 2: target configuration is initialized, tasks are divided into m subtasks, and n virtual machines are preset.
And step 3: initializing global optimization, obtaining an optimal solution bestfittness of an optimal value algorithm of the fitness of the scheduling scheme, a population row bestindex corresponding to the optimal solution, and a best scheduling scheme globalbest, wherein the globalbest is a vector with the length of m.
In step 3, the adaptability of the scheduling scheme includes running time and load balancing.
In the invention, global optimization is initialized so as to obtain an optimal scheduling scheme in the current population, compare the optimal scheduling scheme with subsequent results, and store the scheduling scheme globalbest and the execution time bestfiltness, wherein the bestfiltness can be any index for measuring the quality of the scheduling scheme, such as load balance degree, energy consumption and the like; taking the execution time as an example, the subscript best of the current population optimal scheduling scheme is recorded at the same time, because each row of the matrix is a scheduling scheme, the subscript here is actually the row number of the matrix, and is convenient for subsequent access to the population optimal solution to update the value of globalbest, and globalbest is a value stored by using only one variable in population iteration, and the updating formula is as follows with each iteration:
Figure BDA0002299634250000051
globalbest is only the best of the current population, and the scheduling scheme is updated all the time when population iteration is performed, and there may be a better scheduling scheme in the population P.
And 4, step 4: and performing scheduling operation by using an improved chaotic bat swarm algorithm.
The step 4 comprises the following steps:
step 4.1: initializing a matrix P, wherein each element in the matrix corresponds to a speed, and the speed matrix is V;
in said step 4.1, the initial value of each element in the velocity matrix V is 0.
In said step 4.1, the search measure faid is m-1.
In said step 4.1, the initial value of the proportion Mi of the bat's spatial search moving in the negative direction is 0.
In the step 4.1, the chaotic variable cid is formed by [0,1 ].
Initializing loudness A corresponding to the population quantity p, average loudness Ampan corresponding to the population quantity p, frequency F, maximum element fmax and minimum element fmin of the frequency F, pulse emissivity r, search measure faid, proportion Mi of space search of bats moving towards a negative direction, chaotic factor rid and chaotic variable cid;
step 4.2: initializing data, wherein the loudness A is any value in [0,2], the pulse emissivity r is a uniformly distributed random number between [0,1], and the chaos factor rid is any value in [0,1 ]; let iteration number iter be 1, and maximum iteration number gmax;
step 4.3: if iter is less than gmax, then go to the next step, otherwise, go to step 5;
step 4.4: initializing chaotic variables cid, cid [ i]t＝(cid[i]t-1)1+rid[i]；
Updating the frequency F [ i ] of each bat to fmin + (fmax-fmin) multiplied by β;
updating speed information v [ i ] [ j ] - [ v [ i ] [ j ] + (pop [ i ] [ j ] -globalbest [ j ]) × F [ i ], wherein i is the ith row of P, t is an order identifier, j is the jth column of P, pop [ i ] [ j ] is an element of the jth row of the ith row in the matrix P, and β is a random variable subject to uniform distribution among [0 and 1 ];
step 4.5: calculation of pop [ i][j]t+1＝ω×v[i][j]t+1+(1-0.2×σ)×pop[i][j]tWherein ω and σ are both [0,1]]Random numbers obeying uniform distribution;
step 4.6: let pop [ i ] [ j ] ═ pop [ i ] [ j ] + epsilon × amaan, where epsilon is a random number between [ -1,1] subject to uniform distribution;
chaotic optimization is carried out on the result after the two-dimensional disturbance to obtain
Figure BDA0002299634250000061
Step 4.7: rounding and border crossing judgment are carried out on each element of the matrix P;
step 4.8, updating the frequency F [ i ] ═ fmin + (fmax-fmin) × β;
update loudness
Figure BDA0002299634250000062
Pulse emissivity ri]t+1＝r[i]t(1-e-γt)，
Figure BDA0002299634250000063
And gamma are both [0,1]]Random numbers obeying uniform distribution;
step 4.9: based on step 4.8, updating average loudness ocean, global optimum bestfittness, bestindex and globalbest, iter ═ iter +1, and returning to step 4.3.
In the invention, a group optimization algorithm needs to have a group individual, wherein one row (one array) of a P matrix is regarded as one individual, the P matrix has two types of position attribute and speed attribute, each individual has three types of loudness attribute, pulse emissivity attribute and frequency attribute, the position is a task number on the ith virtual machine of any array, the speed represents the variable quantity of each iteration, the frequency is an attribute for correcting the speed attribute, the loudness attribute is an attribute for correcting the frequency attribute, and the pulse emissivity attribute is used for correcting the loudness attribute; i.e. the position and velocity are matrices of the same dimension and size as the population P, while the pulse emissivity, frequency, loudness are arrays of size population P.
In the invention, the position information is updated by using two-dimensional disturbance, so as to increase the search area and reduce the probability of falling into the local optimum, but if the global optimum is already at the point, the two-dimensional disturbance can cause the search to deviate from the optimum solution, and in order to avoid the oscillation amplitude to be larger, in step 4.5, the oscillation amplitude is set to be 0.2.
In the invention, since the matrix obtained in step 4.6 has floating point numbers, but the virtual machine number is set as an integer, and the size of the task number is also limited, the rounding and border crossing judgment are performed on the result in step 4.7.
And 5: and outputting globalbest as an optimal cloud manufacturing scheduling scheme.
In the invention, the effect is verified based on experiments.
Experiments are carried out by using cloudsim as a simulation platform, and the experimental running environment is a Windows 1064-bit operating system, an Intel Core i5-7500CPU and an 8GB memory;
taking 10 servers as a group, the latter 20, 30 servers are copies of the computing power of the first 10 servers; except for the MIPS (computing power), namely the number of millions of instructions which can be executed per second is different, and the rest data are the same;
the task length is a random integer constant initialized to be 500-10000, in order to remove accidental influence factors and better observe global effects, the average value of 10 times is taken in an experiment, and the iteration times of a group are fixed for 20 times; the running times and corresponding load balances of the best scheduling schemes found by the running of several algorithms are compared as follows:
comparing the change trend of the algorithm, and comparing the change trend with a traditional bat swarm algorithm (BA), a quantum particle swarm algorithm (QPSO) and a Genetic Algorithm (GA), and operating for 10 times, wherein the result is shown in a figure 2; in the figure, the number of the virtual machines is 10, the number of tasks is 30, the number of iterations is 20, and the comparison of the results of multiple running of each algorithm shows that the algorithm of the invention has better stability and stronger optimizing capability, and compared with the algorithm, the GA optimizing capability is not good enough, and the QPSO and BA algorithms have larger variation;
when the number of tasks is 300, the number of virtual machines is 10, the group is iterated 10 times, ram is 8192, bw is 10000, and the operation is performed 21 times, and the result is as shown in fig. 3, and the effect is more obvious.
The method comprises the steps of generating a matrix P of a population, confirming the number of the population, initializing target configuration, dividing tasks into m subtasks, presetting n virtual machines, initializing global optimality to obtain an optimal scheduling scheme, performing scheduling operation by using an improved chaotic bat group algorithm, and outputting a final optimal scheduling scheme globalbest serving as an optimal cloud manufacturing scheduling scheme.
The invention is based on the improved algorithm on the basis of the bat group, the chaos is integrated into the particle motion, so that the bat group moves alternately between chaos and stability and gradually approaches to an optimal point, the calculation precision is improved, and the global optimization capability is further improved; the original algorithm is improved by combining two-dimensional disturbance and the chaotic factor, so that a better effect is achieved.

Claims (8)

1. A cloud manufacturing scheduling method based on an improved chaotic bat swarm algorithm is characterized in that:
the method comprises the following steps:
step 1: generating a population P, wherein P is a matrix and the number of the population is P;
step 2: initializing target configuration, dividing tasks into m subtasks, and presetting n virtual machines;
and step 3: initializing global optimization, obtaining an optimal solution bestfittness of an optimal value algorithm of the fitness of the scheduling scheme, a population row bestindex corresponding to the optimal solution, and an optimal scheduling scheme globalbest, wherein the globalbest is a vector with the length of m;
and 4, step 4: using an improved chaotic bat swarm algorithm to perform scheduling operation;
and 5: and outputting globalbest as an optimal cloud manufacturing scheduling scheme.
2. The cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm as claimed in claim 1, wherein: in step 1, the number of columns of the matrix P is the number of the population, the number of rows is the number of the tasks, and the value of any element is the number of the virtual machine.
3. The cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm as claimed in claim 1, wherein: in step 3, the adaptability of the scheduling scheme includes running time and load balancing.
4. The cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm as claimed in claim 1, wherein: the step 4 comprises the following steps:
step 4.1: initializing a matrix P, wherein each element in the matrix corresponds to a speed, and the speed matrix is V;
initializing loudness A corresponding to the population quantity p, average loudness Ampan corresponding to the population quantity p, frequency F, maximum element fmax and minimum element fmin of the frequency F, pulse emissivity r, search measure faid, proportion Mi of space search of bats moving towards a negative direction, chaotic factor rid and chaotic variable cid;
step 4.2: initializing data, wherein the loudness A is any value in [0,2], the pulse emissivity r is a uniformly distributed random number between [0,1], and the chaos factor rid is any value in [0,1 ]; let iteration number iter be 1, and maximum iteration number gmax;
step 4.3: if iter is less than gmax, then go to the next step, otherwise, go to step 5;
step 4.4: initializing chaotic variables cid, cid [ i]t＝(cid[i]t_1)1+rid[i]；
Updating the frequency F [ i ] of each bat to fmin + (fmax-fmin) multiplied by β;
updating speed information v [ i ] [ j ] ═ v [ i ] [ j ] + (pop [ i ] [ j ] -globalbest [ j ]) × F [ i ];
wherein i is the ith row of P, t is the sequence identifier, j is the jth column of P, pop [ i ] [ j ] is the element of the jth row of the ith row in the matrix P, and β is a random variable subject to uniform distribution among [0,1 ];
step 4.5: calculation of pop [ i][j]t+1＝ω×v[i][j]t+1+(1-0.2×σ)×pop[i][j]tWherein ω and σ are both [0,1]]Random numbers obeying uniform distribution;
step 4.6: let pop [ i ] [ j ] ═ pop [ i ] [ j ] + epsilon × amaan, where epsilon is a random number between [ -1,1] subject to uniform distribution;
chaotic optimization is carried out on the result after the two-dimensional disturbance to obtain
Figure FDA0002299634240000021
Figure FDA0002299634240000022
Step 4.7: rounding and border crossing judgment are carried out on each element of the matrix P;
step 4.8, updating the frequency F [ i ] ═ fmin + (fmax-fmin) × β;
update loudness
Figure FDA0002299634240000031
Pulse emissivity ri]t+1＝r[i]t(1-e-γt)，
Figure FDA0002299634240000032
And gamma are both [0,1]]Random numbers obeying uniform distribution;
step 4.9: based on step 4.8, updating average loudness ocean, global optimum bestfittness, bestindex and globalbest, iter ═ iter +1, and returning to step 4.3.
5. The cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm as claimed in claim 4, wherein: in said step 4.1, the initial value of each element in the velocity matrix V is 0.
6. The cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm as claimed in claim 4, wherein: in said step 4.1, the search measure faid is m-1.
7. The cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm as claimed in claim 4, wherein: in said step 4.1, the initial value of the proportion Mi of the bat's spatial search moving in the negative direction is 0.
8. The cloud manufacturing scheduling method based on the improved chaotic bat swarm algorithm as claimed in claim 4, wherein: in the step 4.1, the chaotic variable cid is formed by [0,1 ].
CN201911216364.2A 2019-12-02 2019-12-02 Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm Pending CN111027665A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911216364.2A CN111027665A (en) 2019-12-02 2019-12-02 Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911216364.2A CN111027665A (en) 2019-12-02 2019-12-02 Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm

Publications (1)

Publication Number Publication Date
CN111027665A true CN111027665A (en) 2020-04-17

Family

ID=70203917

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911216364.2A Pending CN111027665A (en) 2019-12-02 2019-12-02 Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm

Country Status (1)

Country Link
CN (1) CN111027665A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112199172A (en) * 2020-09-25 2021-01-08 桂林理工大学 Hybrid task scheduling method for heterogeneous multi-core processor
CN113222096A (en) * 2021-04-30 2021-08-06 桂林理工大学 Improved particle swarm algorithm for cloud computing task scheduling
CN114358234A (en) * 2021-12-16 2022-04-15 西北大学 Cloud platform resource scheduling method based on improved bat algorithm

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108694077A (en) * 2017-04-10 2018-10-23 中国科学院声学研究所 Based on the distributed system method for scheduling task for improving binary system bat algorithm
CN110412863A (en) * 2019-07-25 2019-11-05 常熟理工学院 Based on the fuzzy PID control method for improving discrete bat algorithm

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108694077A (en) * 2017-04-10 2018-10-23 中国科学院声学研究所 Based on the distributed system method for scheduling task for improving binary system bat algorithm
CN110412863A (en) * 2019-07-25 2019-11-05 常熟理工学院 Based on the fuzzy PID control method for improving discrete bat algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
CHENGFENG JIAN: "An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing" *
简琤峰: "面向边缘计算的改进混沌蝙蝠群协同调度算法" *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112199172A (en) * 2020-09-25 2021-01-08 桂林理工大学 Hybrid task scheduling method for heterogeneous multi-core processor
CN112199172B (en) * 2020-09-25 2024-05-24 桂林理工大学 Heterogeneous multi-core processor-oriented hybrid task scheduling method
CN113222096A (en) * 2021-04-30 2021-08-06 桂林理工大学 Improved particle swarm algorithm for cloud computing task scheduling
CN114358234A (en) * 2021-12-16 2022-04-15 西北大学 Cloud platform resource scheduling method based on improved bat algorithm
CN114358234B (en) * 2021-12-16 2023-12-01 西北大学 Resource scheduling method of cloud platform based on improved bat algorithm

Similar Documents

Publication Publication Date Title
CN110365753B (en) Low-delay load distribution method and device for Internet of things service based on edge calculation
Tang et al. Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems
CN111027665A (en) Cloud manufacturing scheduling method based on improved chaotic bat swarm algorithm
CN106598849B (en) A kind of combined test case generation method based on AP-PSO algorithm
Ning et al. Enhanced particle swarm optimization with multi-swarm and multi-velocity for optimizing high-dimensional problems
CN111325356A (en) Neural network search distributed training system and training method based on evolutionary computation
CN110599068A (en) Cloud resource scheduling method based on particle swarm optimization algorithm
Pooranian et al. Hybrid metaheuristic algorithm for job scheduling on computational grids
CN109445386A (en) A kind of most short production time dispatching method of the cloud manufacturing operation based on ONBA
CN111291854A (en) Artificial bee colony algorithm optimization method based on multiple improved strategies
CN108399105B (en) Software and hardware partitioning method based on improved brainstorming algorithm
CN113722112B (en) Service resource load balancing processing method and system
CN112835684B (en) Virtual machine deployment method for mobile edge computing
CN111488208B (en) Bian Yun collaborative computing node scheduling optimization method based on variable-step-size bat algorithm
CN114662638A (en) Mobile robot path planning method based on improved artificial bee colony algorithm
Pooranian et al. Hybrid pso for independent task scheduling in grid computing to decrease makespan
Xie et al. Cloud computing resource scheduling based on improved differential evolution ant colony algorithm
Zhang et al. Tent chaos and nonlinear convergence factor whale optimization algorithm
CN108182243A (en) A kind of Distributed evolutionary island model parallel method based on Spark
CN110119317B (en) Cloud computing task scheduling method and system based on genetic algorithm
Wang et al. A modified cuckoo search algorithm for flow shop scheduling problem with blocking
Niu et al. Cloud resource scheduling method based on estimation of distirbution shuffled frog leaping algorithm
CN115794330A (en) Cloud computing task scheduling method
CN108805463A (en) A kind of production scheduling method for supporting peak clipping type electricity needs to respond
Nie Research on Task Scheduling Strategy Based on Cloud Computing Environment

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