CN109754057A - Reducer dead weight design method combined with speed disturbance mechanism chaotic locust algorithm - Google Patents
Reducer dead weight design method combined with speed disturbance mechanism chaotic locust algorithm Download PDFInfo
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Abstract
The invention relates to a reducer dead weight design method combining a speed disturbance mechanism chaotic locust algorithm, which comprises the steps of firstly, improving the traditional locust algorithm by adopting a chaotic strategy and a speed disturbance mechanism; and then adopting the locust algorithm improved in the step S1 to solve the dead weight design method of the speed reducer. The invention can better design the speed reducer with smaller self weight.
Description
Technical field
The present invention relates to Design of Speed Reducer field, especially a kind of deceleration of combination velocity disturbance mechanism chaos locust algorithm
Device self weight design method.
Background technique
Locust algorithm (grasshopper optimization algorithm, GOA) is a kind of imitation locust cluster row
For natural heuristic algorithm, be 2017 by Australian scholar Shahrzad Saremi et al. propose novel colony intelligence calculate
Method.Some researcher carries out algorithm improvement and application.
Praveen Tumuluru et al. proposes the locust algorithm being sequentially arranged and for gene selects and cancer
Disease classification.Ali Asghar Heidari et al. is directed to multi-layered perception neural networks, proposes a kind of instruction of combination locust algorithm
Practice method.Devendra Potnuru et al. combines the driving speed of locust algorithm raising brushless direct current motor.In summary,
Locust algorithm there are also some defects and potentiality to be exploited, be worth we improve and application development.
For Design of Speed Reducer problem, this is the nonlinear programming problem under a kind of multi-constraint condition, belongs to np hard problem.
The retarder of smaller self weight is designed after locust algorithm carries out parameter optimization relative to deterministic algorithm.Existing learning aid
Optimization algorithm (teaching-learning-based optimization algorithm, TLBO) is asked for Design of Speed Reducer
Topic, but design effect need to be improved.
Summary of the invention
In view of this, the purpose of the present invention is to propose to a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm from
Redesign method can preferably design the lesser retarder of self weight.
The present invention is realized using following scheme: a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm is from reseting
Meter method, specifically includes the following steps:
Step S1: traditional locust algorithm is improved using chaos strategy and velocity disturbance mechanism；
Step S2: retarder self weight design method is solved using the improved locust algorithm of step S1.
Further, in step S1, traditional locust algorithm is improved using chaos strategy specifically: use
Logistic maps chaos intialization population:
Cn=μ Cn-1(I-Cn-1)
In formula, CnInitial value be 0-1 random number can obtain one group of sequence for being in Complete Chaos when parameter μ=4
Column.
Further, traditional locust algorithm is improved using velocity disturbance mechanism specifically: in traditional locust algorithm
One group of velocity vector of middle introducing and speed more new formula, wherein the speed of individual is defined as d dimensional vector: Vi d=(Vi1,
Vi2,…Vid)；To speed Vi dCarry out random initializtion, the speed and location update formula of i-th of individual are as follows:
In formula, w is inertia weight；R is the numerical value for meeting Gaussian Profile between 0-1.Range is between -0.5 to 0.5.
Further, the value of inertia weight w is 0.9.
Further, the speed V of locust individuali dRange between -0.5 to 0.5.
Further, step S2 specifically includes the following steps:
Step S21: the self weight design problem of retarder is set as being made of four linear restrictions and seven nonlinear restrictions.
The mathematical model of Design of Speed Reducer problem is as follows:
Defined variable are as follows:
Objective function are as follows:
Constraint condition are as follows:
Domain are as follows:
In formula, x1,x2,x3,x4,x5,x6,x7Respectively indicate the width of gear wheel, jackshaft in the output shaft in retarder
Pinion gear width, the bearing in input shaft is at a distance from pinion gear, gear wheel and bearing in the distance between axles of input shaft, output shaft
Distance, the diameter of input shaft and the diameter of output shaft；
Step S22: improved locust algorithm being applied in the solution of above-mentioned mathematical model, and purpose is to minimize target letter
Number obtains the retarder of minimum self weight.
Further, step S22 specifically includes the following steps:
Step S221: all parameters, including Population Size N and the number of iterations t are initialized；
Step S222: chaos intialization population is mapped using Logistic；
Step S223: setting T is optimal solution；
Step S224: judging whether current iteration number is less than maximum number of iterations, if so, entering step S225, otherwise
Enter step S228；
Step S225: the upper bound and the lower bound of solution are checked, and adaptive in more New Tradition locust individual location update formula
Coefficient c；
Step S226: fitness value is calculated according to objective function, and judges whether current individual i is less than Population Size
Otherwise N, enters step S228 if so, entering step S227；
Step S227: defining velocity vector using velocity disturbance mechanism, and scanning frequency of going forward side by side degree updates；Carry out position more simultaneously
Newly；
Step S228: optimal location is substituted into the solution that objective function calculates, i.e. optimal solution exports optimal solution T.
Compared with prior art, the invention has the following beneficial effects: Design of Speed Reducer problem is abstracted into 7 knots by the present invention
The mathematical model of structure parameter, 11 constraint conditions and 1 objective function is used for the optimization design of algorithm.The present invention is calculated for locust
The disadvantages of method is easy to appear precocious phenomenon, and search performance is poor improves locust algorithm using chaos strategy and velocity disturbance mechanism,
General for the Design of Speed Reducer effect of current method, the present invention is asked using improved locust algorithm optimization Design of Speed Reducer
Topic can preferably design the lesser retarder of self weight.
Detailed description of the invention
Fig. 1 is the flow diagram using improved locust algorithm optimization Design of Speed Reducer problem of the embodiment of the present invention.
Fig. 2 is the output result schematic diagram of the embodiment of the present invention.
Fig. 3 is the retarder model schematic of the embodiment of the present invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
It is noted that described further below be all exemplary, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms used herein has usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
As shown in Figure 1, present embodiments providing a kind of retarder self weight of combination velocity disturbance mechanism chaos locust algorithm
Design method, specifically includes the following steps:
Step S1: traditional locust algorithm is improved using chaos strategy and velocity disturbance mechanism；
Step S2: retarder self weight design method is solved using the improved locust algorithm of step S1.
Particularly, traditional locust algorithm specifically:
Locust individual location update formula are as follows:
In formula,It is i-th of locust individual in the position that d is tieed up；Parameter c is adaptation coefficient, more according to formula (2)
Newly；ubdAnd lbdRespectively D ties up the upper bound and the lower bound of search space；S indicates the action intensity between locust individual, according to formula
(3) it updates；dijFor the distance between ith and jth locust, i.e. dij=| xj-xi|；TdFor the optimal location of D dimension.
Wherein, the more new formula of parameter c are as follows:
The range of c is between 0.00001 to 1, cmaxAnd cminThe respectively maximum value and minimum value of adaptation coefficient；T is
Current iteration number, tmaxIt is maximum number of iterations；
Wherein, the update of action intensity s uses following formula:
In formula, f is the intensity of attraction；L is attractive length ratio.
In the present embodiment, in step S1, traditional locust algorithm is improved using chaos strategy specifically: use
Logistic maps chaos intialization population:
Cn=μ Cn-1(1-Cn-1) (4)
In formula, CnInitial value be 0-1 random number can obtain one group of sequence for being in Complete Chaos when parameter μ=4
Column.
In the present embodiment, traditional locust algorithm is improved using velocity disturbance mechanism specifically: in traditional locust
One group of velocity vector and speed more new formula are introduced in algorithm, wherein the speed of individual is defined as d dimensional vector: Vi d=
(Vi1,Vi2,…Vid)；To speed Vi dCarry out random initializtion, the speed and location update formula of i-th of individual are as follows:
In formula, w is inertia weight；R is the numerical value for meeting Gaussian Profile between 0-1.Range is between -0.5 to 0.5.
In the present embodiment, the value of inertia weight w is 0.9.
In the present embodiment, the speed V of locust individuali dRange between -0.5 to 0.5.
In the present embodiment, step S2 specifically includes the following steps:
Step S21: the purpose of Design of Speed Reducer problem is to minimize the self weight of retarder.The self weight of retarder is by gear
Bending stress, the factors such as stress constraint of surface stress, lateral deflection and axis influence.The present embodiment is by the self weight of retarder
Design problem is set as being made of four linear restrictions and seven nonlinear restrictions.The mathematical model of Design of Speed Reducer problem is as follows:
Defined variable are as follows:
Objective function are as follows:
Constraint condition are as follows:
Domain are as follows:
As shown in figure 3, x1Indicate the width of gear wheel in output shaft, x2Indicate the pinion gear width of jackshaft, x3Indicate defeated
Enter the bearing in axis at a distance from pinion gear, x4Indicate the distance between axles of input shaft, x5Indicate gear wheel and bearing in output shaft away from
From x6Indicate the diameter of input shaft, x7Indicate the diameter of output shaft；
Step S22: improved locust algorithm being applied in the solution of above-mentioned mathematical model, and purpose is to minimize target letter
Number obtains the retarder of minimum self weight.
In the present embodiment, step S22 specifically includes the following steps:
Step S221: all parameters, including Population Size N and the number of iterations t are initialized；
Step S222: chaos intialization population is mapped using Logistic；
Step S223: setting T is optimal solution；
Step S224: judging whether current iteration number is less than maximum number of iterations, if so, entering step S225, otherwise
Enter step S228；
Step S225: the upper bound and the lower bound of solution are checked, and adaptive in more New Tradition locust individual location update formula
Coefficient c；
Step S226: fitness value is calculated according to objective function, and judges whether current individual i is less than Population Size
Otherwise N, enters step S228 if so, entering step S227；
Step S227: defining velocity vector using velocity disturbance mechanism, and scanning frequency of going forward side by side degree updates；Carry out position more simultaneously
Newly；
Step S228: optimal location is substituted into the solution that objective function calculates, i.e. optimal solution exports optimal solution T.
Preferably, improved locust algorithm (CV-GOA) is solved the problems, such as Design of Speed Reducer by the present embodiment, and with it is traditional
Locust algorithm (GOA), learning aid optimization algorithm (TLBO) are designed the comparison of result, and data result is as shown in Figure 2.Purpose is
Minimize objective function, the i.e. retarder of the minimum self weight of design.
Program is set as population 25, and maximum number of iterations 200, program repetition running 30 times, output result is as shown in Figure 2.
From Fig. 2 it is recognised that CV-GOA optimizes 7 structural parameters, wherein X1, X5 and X6, X7 have obtained preferable design optimization.It removes
Except this, the design effect of CV-GOA algorithm final goal function is significantly better than other 2 kinds of algorithms, and CV-GOA algorithm is calculated
The smallest target function value, followed by TLBO algorithm are finally GOA algorithms.This means that CV-GOA (the present embodiment method) energy
The problems in Design of Speed Reducer is enough efficiently solved, and the retarder of relatively other smaller self weights of method can be designed.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above described is only a preferred embodiment of the present invention, being not that the invention has other forms of limitations, appoint
What those skilled in the art changed or be modified as possibly also with the technology contents of the disclosure above equivalent variations etc.
Imitate embodiment.But without departing from the technical solutions of the present invention, according to the technical essence of the invention to above embodiments institute
Any simple modification, equivalent variations and the remodeling made, still fall within the protection scope of technical solution of the present invention.
Claims (7)
- The design method 1. retarder of combination velocity disturbance mechanism chaos locust algorithm a kind of is self-possessed, it is characterised in that: including with Lower step:Step S1: traditional locust algorithm is improved using chaos strategy and velocity disturbance mechanism；Step S2: retarder self weight design method is solved using the improved locust algorithm of step S1.
- 2. a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm according to claim 1 is from redesign side Method, it is characterised in that: in step S1, traditional locust algorithm is improved using chaos strategy specifically: use Logistic Map chaos intialization population:Cn=μ Cn-1(1-Cn-1)In formula, CnInitial value be 0-1 random number can obtain one group of sequence for being in Complete Chaos when parameter μ=4.
- 3. a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm according to claim 1 is from redesign side Method, it is characterised in that: traditional locust algorithm is improved using velocity disturbance mechanism specifically: draw in traditional locust algorithm Enter one group of velocity vector and speed more new formula, wherein the speed of individual is defined as d dimensional vector: Vi d=(Vi1,Vi2,… Vid)；To speed Vi dCarry out random initializtion, the speed and location update formula of i-th of individual are as follows:In formula, w is inertia weight；R is the numerical value for meeting Gaussian Profile between 0-1.Range is between -0.5 to 0.5.
- 4. a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm according to claim 3 is from redesign side Method, it is characterised in that: the value of inertia weight w is 0.9.
- 5. a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm according to claim 3 is from redesign side Method, it is characterised in that: the speed V of locust individuali dRange between -0.5 to 0.5.
- 6. a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm according to claim 1 is from redesign side Method, it is characterised in that: step S2 specifically includes the following steps:Step S21: the self weight design problem of retarder is set as being made of four linear restrictions and seven nonlinear restrictions.Slow down The mathematical model of device design problem is as follows:Defined variable are as follows:Objective function are as follows:Constraint condition are as follows:Domain are as follows:In formula, x1,x2,x3,x4,x5,x6,x7Respectively indicate the small tooth of the width of gear wheel in the output shaft in retarder, jackshaft Take turns width, the bearing in input shaft at a distance from pinion gear, in the distance between axles of input shaft, output shaft gear wheel and bearing away from From, the diameter of input shaft and the diameter of output shaft；Step S22: improved locust algorithm being applied in the solution of above-mentioned mathematical model, and purpose is to minimize objective function, Obtain the retarder of minimum self weight.
- 7. a kind of retarder of combination velocity disturbance mechanism chaos locust algorithm according to claim 6 is from redesign side Method, it is characterised in that: step S22 specifically includes the following steps:Step S221: all parameters, including Population Size N and the number of iterations t are initialized；Step S222: chaos intialization population is mapped using Logistic；Step S223: setting T is optimal solution；Step S224: judging whether current iteration number is less than maximum number of iterations, if so, entering step S225, otherwise enters Step S228；Step S225: the upper bound and the lower bound of solution, and the adaptation coefficient in more New Tradition locust individual location update formula are checked c；Step S226: being calculated fitness value according to objective function, and judge whether current individual i is less than Population Size N, if It is then to enter step S227, otherwise, enters step S228；Step S227: defining velocity vector using velocity disturbance mechanism, and scanning frequency of going forward side by side degree updates；Location updating is carried out simultaneously；Step S228: optimal location is substituted into the solution that objective function calculates, i.e. optimal solution exports optimal solution T.
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| CN114117907A (en) * | 2021-11-24 | 2022-03-01 | 大连大学 | Reducer design method based on TQA algorithm |
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