CN112598158A - Task scheduling method of collaborative industrial design platform based on mixed frog leaping algorithm - Google Patents
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Abstract
The invention discloses a task scheduling method of a collaborative industrial design platform based on a mixed frog leaping algorithm, which is characterized by comprising the following steps: step 1, initializing a population; step 2, calculating the fitness and arranging the fitness in a descending order; step 3, sub-population division; step 4, optimizing in the group; step 5, judging whether the iteration times of the sub-population are reached, repeating the process if the iteration times of the sub-population are not reached, and dividing the population again to perform solution space grouping reconstruction if the iteration times of the sub-population are not reached; and 6, judging whether the current solution space meets a termination condition, if not, continuing to calculate, if so, terminating the algorithm, and outputting an optimal solution. The invention is characterized in that: optimal scheduling can be achieved, project cost is minimized, and construction period is shortened to the maximum extent.
Description
Technical Field
The invention relates to the technical field related to a collaborative industrial design platform, in particular to a task scheduling method of a collaborative industrial design platform based on a mixed frog leaping algorithm.
Background
The collaborative industrial design platform adopts a man-machine collaborative design mode based on a design cloud (public cloud or private cloud), the work of a cloud-based industrial design project is divided into parallel tasks and serial tasks, as shown in FIG. 2, part modeling to model assembly are basic parallel tasks and are generally completed manually by designers; rendering and resource optimization are completed by man-machine cooperation; variable binding, animation design and simulation control to process analysis are completed by designers. Because the working hours of all stages of the project are different in consumption, the efficiency of personnel participating in cloud design is also different, the labor cost is settled according to the working hours, the service cost of the agent program is settled according to the consumed electricity cost, and therefore tasks are required to be allocated to the designer and the agent program in real time according to the project progress requirement, the optimal scheduling is realized, the project cost is minimum, and the construction period is shortened to the maximum extent.
Disclosure of Invention
The invention aims to overcome the defects and provides a task scheduling method of a collaborative industrial design platform based on a mixed frog leaping algorithm.
The technical scheme adopted by the invention for realizing the purpose is as follows: a task scheduling method of a collaborative industrial design platform based on a mixed frog leaping algorithm is characterized by comprising the following steps:
step 1, initializing a population: preliminarily setting a certain number of feasible solutions which accord with the constraint function according to empirical data managed by the collaborative industrial design project, wherein each frog is a solution which accords with the constraint function;
step 2, calculating the fitness of each frog, and arranging all the frogs in a descending order according to the fitness;
step 3, sub-population division: randomly dispersing the possible solutions into each solution space and sequencing according to the fitness of the solutions;
step 4, setting an upper limit of the number of times of optimization updating in the group, performing fuzzy evolution within the number of times, randomly generating a new solution based on the maximum leapfrogging step length, and when the absolute value of the difference between the current worst solution and the new solution respectively corresponding to the fitness < = is a fuzzy interval used in the better solution, considering that the new solution is a better solution by the system, updating the worst solution by using a worst solution updating strategy, if the optimal solution is not found within the maximum number of times of optimization updating in the group, randomly generating a new solution based on the maximum leapfging step length to replace the original worst solution, and directly jumping out of the group for optimization regardless of the magnitude comparison of the fitness;
step 5, judging whether the iteration times of the sub-population are reached, repeating the process if the iteration times of the sub-population are not reached, and dividing the population again to perform solution space grouping reconstruction if the iteration times of the sub-population are not reached;
and 6, judging whether the current solution space meets a termination condition, if not, continuing to calculate, if so, terminating the algorithm, and outputting an optimal solution.
The constraint functions comprise a constraint function 1, a constraint function 2 and a constraint function 3;
constraint function 1: for a project of a collaborative industrial design, comprising n jobs W1, W2,…,WnAccording to the plan, the total time D of the project is as follows:
D = DW1 + DW2 + … + DWn
acceptable duration of operation for which DW is set for planning
And a certain work Wn can be decomposed into Wn tasks T, wherein T is to be executed by a designer or an agent, i.e. the tasks assigned to the designer or the agent are at least kept for more than M minutes to ensure that the tasks are valid, and DT is the duration of a certain task T:
DT>= M
and (3) constraint of maximum value of task quantity:
wn <= DWn / M + 1
calculating the total project time based on the tasks:
D =(W1DT1+W1DT2+…+W1DTw1) + (W2DT1+W2DT2+…+W2DTw2) + …
+(WnDT1+WnDT2+…+WnDTwn)
D >= (w1 + w2 + … + wn) * M
constraint function 2: for each task T, let H be the number of design personnel involved, Dh be the man-hour, P be the number of agent program threads involved, Dp be the agent program execution time, the personnel efficiency index be Kh, the agent program performance index be Kp, the maximum number of personnel allocated for a single task be Mh, the maximum number of threads be Mp:
Dh = Kh / H
Dp = Kp / P
WnDT = Khwn / Hwn + Kpwn / Pwn
H <= Mh
P <= Mp
when the number of the participated persons is less or the number of the threads of the allocated agent program is less, the time consumption of the task execution is longer, otherwise, the task execution speed is high, but the cost may be higher
Constraint function 3: and if the labor hour rate is Ch and the electric charge conversion rate is Cp, the single task cost is as follows:
CT = Dh * Ch + Dp * Cp
the total cost of the project is as follows:
C = Ch * (W1Kh1 / W1H1 + W1Kh2 / W1H2 + … + W1Khw1 / W1Hw1)
+ Ch * (W2Kh1 / W2H1 + W2Kh2 / W2H2 + … + W2Khw2 / W1Hw2)
+ …
+ Ch * (WnKh1 / WnH1 + WnKh2 / WnH2 + … + WnKhwn / WnHwn)
+ Cp * (W1Kp1 / W1P1 + W1Kp2 / W1P2 + … + W1Kpw1 / W1Pw1)
+ Cp * (W2Kp1 / W2P1 + W2Kp2 / W2P2 + … + W2Kpw2 / W1Pw2)
+ …
+ Cp * (WnKp1 / WnP1 + WnKp2 / WnP2 + … + WnKpwn / WnPwn)
C < MC
MC is the project total budget.
The fitness function is set as:
F(W, DT, H, P) = [Sigma(WiDTwi)] / [Sigma(WiHwi) + Sigma(WiPwi) ]
where i =1 to n, wi < = DWi/M + 1.
The invention is characterized in that: optimal scheduling can be achieved, project cost is minimized, and construction period is shortened to the maximum extent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a task diagram of a cloud-based industrial design project.
Detailed Description
As shown in fig. 1, the present invention is a cooperative industrial design platform task scheduling method based on a mixed frog leap algorithm, including:
step 1, initializing a population: the population scale is the number of solutions obtained by each iteration of the algorithm, a certain number of feasible solutions conforming to the constraint function are preliminarily set according to the empirical data managed by the collaborative industrial design project, and each frog is a solution conforming to the constraint function, namely the number of sub-populations is set, and the iteration depth of the algorithm is set;
the constraint functions comprise a constraint function 1, a constraint function 2 and a constraint function 3;
constraint function 1: for a project of a collaborative industrial design, comprising n jobs W1, W2,…,WnAccording to the plan, the total time D of the project is as follows:
D = DW1 + DW2 + … + DWn
DW an acceptable duration of operation for a planned setting, i.e., the maximum allowable duration of a particular operation
And a certain work Wn can be decomposed into Wn tasks T, which are executed by designers or agents, that is, the tasks allocated to the designers or agents can be ensured to be valid only by keeping for at least M minutes (the shortest execution time), and DT is set as the duration of a certain task T:
DT>= M
and (3) constraint of maximum value of task quantity:
wn <= DWn / M + 1
calculating the total project time based on the tasks:
D =(W1DT1+W1DT2+…+W1DTw1) + (W2DT1+W2DT2+…+W2DTw2) + …
+(WnDT1+WnDT2+…+WnDTwn)
D >= (w1 + w2 + … + wn) * M
constraint function 2: for each task T, let H be the number of design personnel involved, Dh be the man-hour, P be the number of agent program threads involved, Dp be the execution time of the agent program, the personnel efficiency index be Kh (empirical value), the agent program performance index be Kp (empirical value), the maximum number of personnel allocated for a single task be Mh, the maximum number of threads be Mp:
Dh = Kh / H
Dp = Kp / P
WnDT = Khwn / Hwn + Kpwn / Pwn
H <= Mh
P <= Mp
when the number of the participating personnel is less or the number of the threads of the allocated agent program is less, the time consumption of task execution is longer, otherwise, the task execution speed is high, but the cost is possibly higher;
constraint function 3: assuming that the man-hour rate is Ch (empirical value) and the electricity charge conversion rate is Cp (empirical value), the individual task charge:
CT = Dh * Ch + Dp * Cp
the total cost of the project is as follows:
C = Ch * (W1Kh1 / W1H1 + W1Kh2 / W1H2 + … + W1Khw1 / W1Hw1)
+ Ch * (W2Kh1 / W2H1 + W2Kh2 / W2H2 + … + W2Khw2 / W1Hw2)
+ …
+ Ch * (WnKh1 / WnH1 + WnKh2 / WnH2 + … + WnKhwn / WnHwn)
+ Cp * (W1Kp1 / W1P1 + W1Kp2 / W1P2 + … + W1Kpw1 / W1Pw1)
+ Cp * (W2Kp1 / W2P1 + W2Kp2 / W2P2 + … + W2Kpw2 / W1Pw2)
+ …
+ Cp * (WnKp1 / WnP1 + WnKp2 / WnP2 + … + WnKpwn / WnPwn)
c < MC (total budget for the project);
step 2, calculating the fitness of each frog, and arranging all the frogs in a descending order according to the fitness;
the function of fitness in the algorithm is set as:
F(W, DT, H, P) = [Sigma(WiDTwi)] / [Sigma(WiHwi) + Sigma(WiPwi) ]
where i =1 to n, wi < = DWi/M + 1;
step 3, sub-population division: randomly dispersing the possible solutions into various solution spaces and ordering according to the fitness of the solutions, and appearing as if frogs jump from one group to another;
step 4, in order to enable the algorithm not to fall into the dead cycle, setting an upper limit of the optimization updating times in the group, carrying out fuzzy evolution within the times, randomly generating a new solution based on the maximum leapfrog step length, when the absolute value of the difference between the current worst solution and the new solution respectively corresponding to the fitness is < = a fuzzy interval used when the optimal solution is taken, considering that the new solution is a more optimal solution by the system, updating the worst solution by using a worst solution updating strategy, if the optimal solution is not found within the maximum times of the optimization updating in the group, randomly generating a new solution to replace the original worst solution based on the maximum leapfrog step length, and directly jumping out of the group for optimization without considering the fitness size comparison;
step 5, judging whether the iteration times of the sub-population are reached, repeating the process if the iteration times of the sub-population are not reached, and dividing the population again to perform solution space grouping reconstruction if the iteration times of the sub-population are not reached;
and 6, judging whether the current solution space meets a termination condition, if not, continuing to calculate, if so, terminating the algorithm, outputting the optimal solution, and ending the algorithm.
The method is based on a mixed frog leap (SFLA) algorithm, SFLA initial parameters are configured according to task characteristics of a collaborative industrial design platform, meanwhile, task scheduling data in a historical project management database and cache data generated in real time by a current project are called, and system fitness under various constraint conditions is analyzed and calculated in real time, so that a set of optimized collaborative industrial design platform task scheduling scheme is obtained.
The algorithm core of the invention lies in how to schedule and balance the number of human-machine tasks, shortens the construction period to the greatest extent under the permission of project budget, and simultaneously saves the parameter configuration of historical scheduling as an experience scheme into a historical database for the next time when the algorithm initializes a feasible solution, forms a scheduling knowledge base, guides the selection of fitness functions and updating rules, and avoids the occurrence of local optimization to cause that an optimal scheduling scheme cannot be obtained.
When the method is used, a project manager can configure corresponding project budget, project plan, work decomposition (decomposition into tasks), shortest effective period of tasks, personnel number, personnel efficiency index, available thread number of agent programs, agent program execution performance, man-hour rate, electricity charge conversion rate and other parameters according to project requirements, and initializes input sample space, grouping number, frog number of each group, local iteration number, mixed iteration number, realization number, maximum movement step length of solution and other parameters before task allocation scheduling of designers and agent programs, and a background algorithm can utilize real-time calling cache data to carry out calculation analysis according to the parameters required in the algorithm, and finally can obtain a set of optimized task allocation scheduling scheme in a short time. In the calculation process, the system page displays the optimal frog swarm adaptive value obtained in each experiment in real time, the average value of the frog swarm adaptive values is obtained after all calculations are finished, and finally the shortest time of the remaining execution of the project is obtained, namely the optimal real-time task scheduling scheme.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (3)
1. A task scheduling method of a collaborative industrial design platform based on a mixed frog leaping algorithm is characterized by comprising the following steps:
step 1, initializing a population: preliminarily setting a certain number of feasible solutions which accord with the constraint function according to empirical data managed by the collaborative industrial design project, wherein each frog is a solution which accords with the constraint function;
step 2, calculating the fitness of each frog, and arranging all the frogs in a descending order according to the fitness;
step 3, sub-population division: randomly dispersing the possible solutions into each solution space and sequencing according to the fitness of the solutions;
step 4, setting an upper limit of the number of times of optimization updating in the group, performing fuzzy evolution within the number of times, randomly generating a new solution based on the maximum leapfrogging step length, and when the absolute value of the difference between the current worst solution and the new solution respectively corresponding to the fitness < = is a fuzzy interval used in the better solution, considering that the new solution is a better solution by the system, updating the worst solution by using a worst solution updating strategy, if the optimal solution is not found within the maximum number of times of optimization updating in the group, randomly generating a new solution based on the maximum leapfging step length to replace the original worst solution, and directly jumping out of the group for optimization regardless of the magnitude comparison of the fitness;
step 5, judging whether the iteration times of the sub-population are reached, repeating the process if the iteration times of the sub-population are not reached, and dividing the population again to perform solution space grouping reconstruction if the iteration times of the sub-population are not reached;
and 6, judging whether the current solution space meets a termination condition, if not, continuing to calculate, if so, terminating the algorithm, and outputting an optimal solution.
2. The hybrid frog-leap algorithm based collaborative industrial design platform task scheduling method according to claim 1, wherein the constraint functions comprise constraint function 1, constraint function 2, constraint function 3;
constraint function 1: for a project of a collaborative industrial design, comprising n jobs W1, W2,…,WnAccording to the plan, the total time D of the project is as follows:
D = DW1 + DW2 + … + DWn
acceptable duration of operation for which DW is set for planning
And a certain work Wn can be decomposed into Wn tasks T, wherein T is to be executed by a designer or an agent, i.e. the tasks assigned to the designer or the agent are at least kept for more than M minutes to ensure that the tasks are valid, and DT is the duration of a certain task T:
DT>= M
and (3) constraint of maximum value of task quantity:
wn <= DWn / M + 1
calculating the total project time based on the tasks:
D =(W1DT1+W1DT2+…+W1DTw1) + (W2DT1+W2DT2+…+W2DTw2) + …
+(WnDT1+WnDT2+…+WnDTwn)
D >= (w1 + w2 + … + wn) * M
constraint function 2: for each task T, let H be the number of design personnel involved, Dh be the man-hour, P be the number of agent program threads involved, Dp be the agent program execution time, the personnel efficiency index be Kh, the agent program performance index be Kp, the maximum number of personnel allocated for a single task be Mh, the maximum number of threads be Mp:
Dh = Kh / H
Dp = Kp / P
WnDT = Khwn / Hwn + Kpwn / Pwn
H <= Mh
P <= Mp
when the number of the participated persons is less or the number of the threads of the allocated agent program is less, the time consumption of the task execution is longer, otherwise, the task execution speed is high, but the cost may be higher
Constraint function 3: and if the labor hour rate is Ch and the electric charge conversion rate is Cp, the single task cost is as follows:
CT = Dh * Ch + Dp * Cp
the total cost of the project is as follows:
C = Ch * (W1Kh1 / W1H1 + W1Kh2 / W1H2 + … + W1Khw1 / W1Hw1)
+ Ch * (W2Kh1 / W2H1 + W2Kh2 / W2H2 + … + W2Khw2 / W1Hw2)
+ …
+ Ch * (WnKh1 / WnH1 + WnKh2 / WnH2 + … + WnKhwn / WnHwn)
+ Cp * (W1Kp1 / W1P1 + W1Kp2 / W1P2 + … + W1Kpw1 / W1Pw1)
+ Cp * (W2Kp1 / W2P1 + W2Kp2 / W2P2 + … + W2Kpw2 / W1Pw2)
+ …
+ Cp * (WnKp1 / WnP1 + WnKp2 / WnP2 + … + WnKpwn / WnPwn)
C < MC
MC is the project total budget.
3. The cooperative industrial design platform task scheduling method based on the mixed frog-leaping algorithm as claimed in claim 1, wherein the function of the fitness is set as:
F(W, DT, H, P) = [Sigma(WiDTwi)] / [Sigma(WiHwi) + Sigma(WiPwi) ]
where i =1 to n, wi < = DWi/M + 1.
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