CN109214499A - A kind of difference searching algorithm improving optimizing strategy - Google Patents
A kind of difference searching algorithm improving optimizing strategy Download PDFInfo
- Publication number
- CN109214499A CN109214499A CN201810842197.1A CN201810842197A CN109214499A CN 109214499 A CN109214499 A CN 109214499A CN 201810842197 A CN201810842197 A CN 201810842197A CN 109214499 A CN109214499 A CN 109214499A
- Authority
- CN
- China
- Prior art keywords
- algorithm
- iteration
- value
- function
- search range
- 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
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 11
- 238000012360 testing method Methods 0.000 claims description 11
- 230000008859 change Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 2
- 230000005012 migration Effects 0.000 description 2
- 238000013508 migration Methods 0.000 description 2
- 241000234295 Musa Species 0.000 description 1
- 235000018290 Musa x paradisiaca Nutrition 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 241000902900 cellular organisms Species 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
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]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of difference searching algorithms for improving optimizing strategy, mainly change the optimizing strategy that original error divides searching algorithm, specifically improve virtual individual active mode and algorithm Search Range.Firstly, the Search Range that the present invention is set according to algorithm generates an initial point position, which, which is referred to as, is inhabited a little, and the value for inhabiting a little is substituted for global optimum.Then, algorithm according to the present invention in improved optimizing strategy carry out continuous iteration and update to operate, the candidate value generated after each iteration is compared with global optimum, selects preferably to solve as global optimum, be updated until meeting stopping criterion for iteration repeatedly.In the process, if the candidate value generated after iteration has exceeded the search range that algorithm is set, exercise boundary limits method.The present invention improves the low optimization accuracy of former algorithm, while also accelerating the speed of algorithm optimizing, can more quickly and accurately find out the optimal value of parameter to be optimized.
Description
Technical field
The present invention relates to a kind of optimization method of improvement difference search, in particular to a kind of difference for improving optimizing strategy is searched
Rope algorithm belongs to intelligence computation field.
Background technique
The creation inspiration of difference searching algorithm (Differential Search Algorithm, DSA) derives from nature
Different kind organism body migration course.The algorithm has been continued to use it and has been randomly generated initially using differential evolution algorithm as basic structure
The method of position and boundary limitation, and Brownian movement has been used to simulate random motion of the organism in migration course, it uses
The mode of Random Activation virtual individual carrys out the update of problem of modelling dimension, is a kind of newer, efficient optimizing algorithm.
During evolution, difference searching algorithm easily falls into local extremum, and main cause is the direction mistake that biota is searched
In diverging, cause whole fluctuation range very big, is not easy to optimize along optimal path.It is random due to Brownian movement
Property, algorithm are easily trapped into local extremum in the iteration later period, are not easy to find peak value, and optimum point often has not yet been reached in searching process
It just stopped iteration.The present invention proposes a kind of difference searching algorithm for improving optimizing strategy to solve the above-mentioned problems, solves
Former algorithm is easily trapped into the problems such as local extremum and long calculating time.
Summary of the invention
The invention discloses a kind of difference searching algorithms for improving optimizing strategy, mainly change original error and divide searching algorithm
Optimizing strategy specially improves virtual individual active mode and algorithm Search Range.Firstly, the present invention is set according to algorithm
Search Range generate an initial point position, which, which is referred to as, inhabites a little, and the value for inhabiting a little is substituted for global optimum
Value.Then, algorithm according to the present invention in improved method carry out continuous iteration and update to operate, by what is generated after each iteration
Candidate value is compared with global optimum, is selected preferably to solve as global optimum, be updated repeatedly until meeting iteration end
Only until condition.In the process, if the candidate value generated after iteration has exceeded the search range that algorithm is set, side is executed
Boundary determines method.
Specific step is as follows for the difference searching algorithm for improving optimizing strategy:
Step 1: setting initial parameter: setting search range, the global optimum that algorithm generates search for model without departing from this
It encloses, sets the dimension of quantity and optimization problem individual in population, set the number of iterations or termination condition, set optimizing mould
Formula, and set timing program；
Step 2: the population for improving the difference searching algorithm of optimizing strategy can be randomly generated one initially according to search range
Position, which, which is referred to as, inhabites a little, and the value for inhabiting a little is substituted for global optimum.Because optimal value is to produce at random thus
It is raw, so it is last solution that optimal value is almost impossible；
Step 3: judging whether optimization process at this time reaches preset iterations max or iterative value equal to objective function
Value, carries out next round iteration if being unsatisfactory for；
Step 4: the difference searching algorithm for improving optimizing strategy can be next according to Brownian movement stochastic evolution population position
The population position of evolution is referred to as dwell point, the candidate value that thus this wheel iteration of available one of dwell point generates；It is specific:
Stopover=Pos+Rmap (Dir-Pos)
Wherein, Stopover is dwell point, indicates virtual superior biological body current location；R is the random of movement setting
Value, for simulating Blang's random motion；Map is one and forms selector by 0 and 1 by what problem dimension was constituted, and 0 represents the problem
Direction is taken turns herein does not execute search in iteration, 1 is on the contrary；Pos indicates the position that random selection individual is migrated；Dir-Pos indicates super
The direction that grade organism migrates.
The difference searching algorithm for improving optimizing strategy has adjusted using Rmap as the iterative manner of optimizing strategy, specific:
Original error divides the R candidate value of searching algorithm to have 5, is respectively: R1=4*randn；R2=4*randg；R3=
Lognrnd (rand, 5*rand)；R4=1./gamrnd (1,0.5)；R5=1/normrnd (0,5), wherein rand representative function
What is generated is the pseudo random number between 0 to 1；Randn indicates that mean value is the normal distribution that 0 variance is 1；Randg indicates ruler
Degree parameter and form parameter are 1 Gamma distribution；Lognrnd indicates logarithm normal distribution；Gamrnd indicates Gamma distribution；
Normrnd indicates normal distribution.Above-mentioned R1-R5Value all have the characteristics that fluctuation range is larger and unstable.In the present invention
It is required that the search range of R becomes smaller and can be more stable.In the present invention, R is assigned R=2*rand.
In difference searching algorithm, map simulates the more new state of problem dimension, and former algorithm is more biased towards in updating a certain ask
The dimension of topic, and it is possible to not replacement problem dimension；The present invention is more heavily weighted toward while updating the dimension of multiple problems, and eliminates
Not the case where not updating dimension.
Step 5: exercise boundary limits method (inessential), the borders method in difference searching algorithm derived from difference into
Change algorithm, because often generating certain candidate values beyond search range in optimization process, for these invalid candidate values,
This step replaces the candidate value beyond search range by way of random assignment, and the value that this borders generates is replaced
Change the candidate value that wheel iteration generates thus into；If the candidate solution continues to execute step 6 without departing from search range；
Step 6: whether the candidate value for judging that this wheel iteration generates is better than existing global optimum, if YES then this is waited
Choosing value is substituted for global optimum, if it is otherwise, not replacing.In the present invention, judge that condition whether candidate value is superior refers to,
Candidate value whether than global optimum existing in algorithm closer to the global optimum of test function；
Step 7: repeating step 3-6 until meeting the iteration termination target of step 3, finally export global optimum at this time
Value, and timing is terminated, obtain the time of algorithm operation.
Beneficial effects of the present invention: the difference searching algorithm for improving optimizing strategy improves the optimizing that original error divides searching algorithm
Precision, while also accelerating the speed of algorithm optimizing compensates for primal algorithm and easily falls into local extremum, Premature Convergence or iteration and stops
The defects of stagnant, can more quickly and accurately find out the optimal value of parameter to be optimized.
Detailed description of the invention
Fig. 1 is the overview flow chart for the difference searching algorithm that the present invention improves optimizing strategy；
Fig. 2 is the function curve diagram of the Ackley function that uses in the present invention under two-dimensional problems；
Fig. 3 is the function curve diagram of the Griewank function that uses in the present invention under two-dimensional problems；
Fig. 4 is the function curve diagram of the Rastrigin function that uses in the present invention under two-dimensional problems；
Fig. 5 is the function curve diagram of the Rosenbrock function that uses in the present invention under two-dimensional problems；
Fig. 6 is the function curve diagram of the Sphere function that uses in the present invention under two-dimensional problems；
Fig. 7 is the function curve diagram of the Zakharov function that uses in the present invention under two-dimensional problems.
Specific embodiment
Embodiment 1: as shown in Figure 1, the present invention generates an initial point position according to the Search Range that algorithm is set, it should
Position, which is referred to as, to be inhabited a little, and the value for inhabiting a little is substituted for global optimum.Then, algorithm according to the present invention in improved side
Method carries out continuous iteration and updates to operate, and the candidate value generated after each iteration is compared with global optimum, is selected
Preferably solution is used as global optimum, is updated until meeting stopping criterion for iteration repeatedly.In the process, if being produced after iteration
Raw candidate value has exceeded the search range that algorithm is set, then exercise boundary limits method.
Specific step is as follows for the difference searching algorithm for improving optimizing strategy:
Step 1: setting initial parameter: setting search range, the global optimum that algorithm generates search for model without departing from this
It encloses.The dimension of quantity and optimization problem individual in population is set, the number of iterations or termination condition are set, sets optimizing mould
Formula, and set timing program.In the present invention, for all unconstrained optimization test functions, the difference of optimizing strategy is improved
Divide searching algorithm and original error that the search range of searching algorithm (DSA) is divided uniformly to be set as [- 10,10], individual quantity i=50,
The dimension d=3 of optimization problem, the number of iterations G=100.Selected test function, such as Sphere function is selected to test letter
Number, function curve diagram of the function under two-dimensional problems is as shown in fig. 7, its function expression are as follows:
Wherein, F (x) is required non trivial solution, and d is the dimension of target problem, and i is the number of optimizing particle in algorithm,
In this test function, when x=(0,0,0 ..., 0)TWhen, there is globally optimal solution minF (x)=0.
Step 2: the population for improving the difference searching algorithm of optimizing strategy can be randomly generated one initially according to search range
Position, which, which is referred to as, inhabites a little, and the value for inhabiting a little is substituted for global optimum.Because optimal value is to produce at random thus
It is raw, so it is last solution that optimal value is almost impossible, it is specific:
popmn=rand (upn-lown)+lown
Wherein, the scale pop of populationm, m={ 1,2 ..., S }, wherein S indicates individual sum, total dimension popn, n=1,
2 ..., D }, wherein D indicates the dimension of institute's optimization problem.What rand representative function generated is the pseudo random number between 0 to 1,
upnAnd lownRespectively indicate the upper bound and the lower bound of preset search range.
Step 3: judging whether optimization process at this time meets the condition of iteration termination or meet iteration ends target, if not
Satisfaction then carries out next round iteration；
Step 4: the difference searching algorithm for improving optimizing strategy can be next according to Brownian movement stochastic evolution population position
The population position of evolution is referred to as dwell point, the candidate value that thus this wheel iteration of available one of dwell point generates, specific:
Stopover=Pos+Rmap (Dir-Pos)
Wherein, Stopover is dwell point, indicates virtual superior biological body current location；R is the random of movement setting
Value, for simulating Blang's random motion；Map is one and forms selector by 0 and 1 by what problem dimension was constituted, and 0 represents the problem
Direction is taken turns herein does not execute search in iteration, 1 is on the contrary；Pos indicates the position that random selection individual is migrated；Dir-Pos indicates super
The direction that grade organism migrates.
Step 5: judging whether the candidate value generated by step 4 has exceeded pre-set search range, if then holding
Row bound limits method, and the value that this borders generates is substituted for the candidate value that wheel iteration generates thus；If it is not, then continuing
Step 6 is executed, specific:
Sitemn=rand (upn-lown)+low n
Wherein, SitemnFor the position of virtual population, m={ 1,2 ..., S }, wherein S indicates that individual is total, n=1,
2 ..., D }, wherein D indicates the dimension of institute's optimization problem.Work as Sitemn<lownOr Sitemn>upnWhen, to SitemnAccording to above formula into
The distribution of row random site.That rand representative function generates is the pseudo random number between 0 to 1, upnAnd lownIt respectively indicates pre-
The upper bound of the search range first set and lower bound.
Step 6: whether the candidate value for judging that this wheel iteration generates is better than existing global optimum, if then by this candidate
Value is substituted for global optimum, if otherwise not replacing；
Step 7: repeating step 3-6 until meeting the iteration termination target of step 3, finally export global optimum at this time
Value, and timing is terminated, obtain the time of algorithm operation；
Embodiment 2: as shown in figs. 1-7, present invention incorporates attached drawings and specific implementation case to do to technical solution of the present invention
It is described in further detail.The present invention is to improve the difference searching algorithm of optimizing strategy, in unconstrained optimization problem or is had about
On beam Global Optimal Problem, optimal solution or feasible solution can be obtained.Several examples in unconstrained optimization problem are given below
Son, how to illustrate using the difference searching algorithm of the invention for improving optimizing strategy.Ackley, Griewank are selected,
The typical extreme value of a function problem of Rastrigin, Rosenbrock, Sphere, Zakharov, Hartmann this 7, with the present invention
The difference searching algorithm and original error for improving optimizing strategy divide searching algorithm (DSA) to carry out test and comparison.
(1) Ackley function:
Ackley function is the experiment function of continuous type obtained from the cosine moderately amplified on index function superposition, and belonging to has
The function of many local extremums.It is characterized in that one almost flat region modulated by cosine wave and form hole one by one or peak, from
And keep curved surface uneven.Ackley points out that the search of this function is sufficiently complex, because a stringent suboptimization is calculated
Method inevitably falls into the trap of local optimum in hill climbing process；And the mountain of interference can be crossed by scanning larger field
Paddy progressively reaches preferable optimum point.The function when optimizing, works as x in the region of search of restrictioniWhen=0, most global minima is obtained
Value 0.
(2) Griewank function:
The function belongs to the function there are many local extremum.The number of local extremum and the dimension of problem are related, this function
It is typical non-linear multi-modal function, there is extensive search space, it is very intractable multiple to be typically considered optimization algorithm
Miscellaneous multi-modal problem.The function when optimizing, works as x in the region of search of restrictioniWhen=0, most global minimum 0 is obtained.
(3) Rastrigin function:
The function belongs to the function there are many local extremum, is the function of a multi-peak, there are a large amount of local minimums
And maximum of points, it is a kind of typical nonlinear multi-modal function, peak shape is in the appearance of height ups and downs jumping characteristic, to intelligence
Energy algorithm has duplicity, and algorithm is made easily to fall into local extremum.The function when optimizing, works as x in the region of search of restrictioniWhen=0,
Obtain most global minimum 0.
(4) Rosenbrock function:
The function belongs to the function of valleys, the also known as unimodal function of Banana Type, is a kind of non-convex, pathological function,
There are interactional effect between correlated variables, cause what many optimization algorithms can not prepare to find global optimum, it should
Function when optimizing, works as x in the region of search of restrictioniWhen=1, most global minimum 0 is obtained.
(5) Sphere function:
This function belongs to bowl-shape type function, is a simple unimodal function, function is interior, and there is no Local Extremums, mainly
Intelligent algorithm is investigated for the precise search ability of extreme point.The function when optimizing, works as x in the region of search of restrictioniWhen=0,
Obtain most global minimum 0.
(6) Zakharov function:
This function belongs to smooth type function, is a simple unimodal function, which has no other than global minimum
Other local minimum points work as x in the region of search of restriction when optimizingiWhen=0, which obtains most global minimum 0.
(7) Hartmann function:
Hartmann function can be used for 3 dimensions or the optimizing of 6 dimensions solves, and what is be used in the present invention is 3
The problem of a dimension, solves, wherein preset The function in the region of search of restriction when optimizing, when x=(0.114614,
0.555649,0.852547) when, most global minimum -3.86278 is obtained.
Test result: according to 1 step of embodiment, test is brought into using 7 functions in embodiment 2, to each function
50 simulation trials are carried out alone, obtained data are counted, and are finally obtained suitable between the method for the present invention and original method
Angle value (optimal, worst, average, time) is answered, as shown in table 1:
Table 1: the method for the present invention and original error divide between searching algorithm data compared with fitness
As it can be seen from table 1 inventive algorithm is in 7 kinds of different function tests, the function embodied is adapted to
Angle value (optimal, worst, average, time), the original error that compares divides for searching algorithm, and optimizing effect is better, numerically
Close to the theoretical extreme of function, in the test of the functions such as Ackley, calculating speed is also superior to former algorithm.By in embodiment 2 to 7
The performance test of functional equation, it was demonstrated that the optimizing performance of the difference searching algorithm of the invention for improving optimizing strategy is compared with original error point
For searching algorithm, there is very big raising.
Although above-mentioned experimental section and attached drawing part have carried out certain description to the present invention, the present invention does not limit to
In above-mentioned specific embodiment, described above to belong to be illustrative nature, is not belonging to restrictive.The ordinary skill people of this field
Member under the inspiration of the present invention, without deviating from the spirit of the invention, can also make many changes to inventive algorithm
Shape, these are belonged within present invention protection.
Claims (4)
1. a kind of difference searching algorithm for improving optimizing strategy, which comprises the following steps:
Initial parameter is arranged in step 1: setting search range sets the dimension of quantity and optimization problem individual in population, if
Determine the number of iterations or termination condition, sets optimizing mode, and set timing program；
Step 2 is randomly generated an initial position according to search range and inhabites a little, and by the value for inhabiting a little be substituted for it is global most
The figure of merit；
The optimization process of step 3 judgement at this time whether reaches preset iterations max or iterative value is equal to target function value, if
It is unsatisfactory for, carries out next round iteration；
Step 4 is referred to as dwell point according to Brownian movement stochastic evolution population position, the population position of next evolution, thus
The candidate value that this wheel iteration of available one of dwell point generates；
Step 5 judges whether the candidate value generated by step 4 has exceeded pre-set search range, if so then execute side
Boundary determines method, and the value that this borders generates is substituted for the candidate value that wheel iteration generates thus；If it is not, then continuing to execute
Step 6；
Whether the candidate value that step 6 judges that this wheel iteration generates is better than whether existing global optimum i.e. candidate value compares algorithm
In existing global optimum closer to test function global optimum, if this candidate value is then substituted for global optimum,
If otherwise not replacing；
Step 7 repeats step 3-6 until meeting the iteration termination target of step 3, finally exports global optimum at this time, and
Timing is terminated, the time of algorithm operation is obtained.
2. the difference searching algorithm according to claim 1 for improving optimizing strategy, it is characterised in that: dwell in the step 2
Cease the random generation process of point are as follows:
popmn=rand (upn-lown)+lown
Wherein, the position inhabited a little is popmn, m={ 1,2 ..., S }, S indicate individual sum in population, n=1,2 ...,
D }, D indicates the dimension of optimization problem, and that rand representative function generates is the pseudo random number between 0 to 1, upnAnd lownPoint
The upper bound and the lower bound of preset search range are not indicated.
3. the difference searching algorithm according to claim 1 for improving optimizing strategy, it is characterised in that: the tool of the step 4
Body process are as follows:
Stopover=Pos+Rmap (Dir-Pos)
Wherein, Stopover is dwell point, indicates virtual superior biological body current location；R is the random value of movement setting, is used for
Blang's random motion is simulated, R=2*rand, wherein what rand representative function generated is the pseudo random number between 0 to 1；map
It is one and selector is formed by 0 and 1 by what problem dimension was constituted, 0, which represents the problem direction, takes turns do not execute search in iteration herein,
1 is on the contrary；Pos indicates the position that random selection individual is migrated；Dir-Pos indicates the direction that superior biological body is migrated.
4. the difference searching algorithm according to claim 1 for improving optimizing strategy, it is characterised in that: in the step 5
Borders method replaces the candidate value beyond search range specifically by the mode of random assignment, and this value is replaced
The candidate value generated as this wheel iteration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810842197.1A CN109214499A (en) | 2018-07-27 | 2018-07-27 | A kind of difference searching algorithm improving optimizing strategy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810842197.1A CN109214499A (en) | 2018-07-27 | 2018-07-27 | A kind of difference searching algorithm improving optimizing strategy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109214499A true CN109214499A (en) | 2019-01-15 |
Family
ID=64990313
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810842197.1A Pending CN109214499A (en) | 2018-07-27 | 2018-07-27 | A kind of difference searching algorithm improving optimizing strategy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109214499A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108761282A (en) * | 2018-04-18 | 2018-11-06 | 国网江苏省电力有限公司电力科学研究院 | A kind of ultrasonic wave shelf depreciation auto-check system and its method based on robot |
| CN111062962A (en) * | 2019-12-06 | 2020-04-24 | 昆明理工大学 | Multi-threshold ultrasonic image segmentation method based on differential search algorithm |
| CN112199996A (en) * | 2020-09-04 | 2021-01-08 | 西安交通大学 | Rolling bearing diagnosis method based on parameter self-adaptive VMD and fast Hoyer spectrogram indexes |
| CN112736912A (en) * | 2020-12-28 | 2021-04-30 | 上海电力大学 | Distribution network reconstruction method based on annealing brownian motion and single ring optimization |
| CN113722853A (en) * | 2021-08-30 | 2021-11-30 | 河南大学 | Intelligent calculation-oriented group intelligent evolutionary optimization method |
| CN117970228A (en) * | 2024-03-28 | 2024-05-03 | 中国人民解放军火箭军工程大学 | Multi-target DOA estimation method based on uniform circular array |
-
2018
- 2018-07-27 CN CN201810842197.1A patent/CN109214499A/en active Pending
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108761282A (en) * | 2018-04-18 | 2018-11-06 | 国网江苏省电力有限公司电力科学研究院 | A kind of ultrasonic wave shelf depreciation auto-check system and its method based on robot |
| CN108761282B (en) * | 2018-04-18 | 2024-01-05 | 国网江苏省电力有限公司电力科学研究院 | Ultrasonic partial discharge automatic diagnosis system and method based on robot |
| CN111062962A (en) * | 2019-12-06 | 2020-04-24 | 昆明理工大学 | Multi-threshold ultrasonic image segmentation method based on differential search algorithm |
| CN111062962B (en) * | 2019-12-06 | 2022-09-27 | 昆明理工大学 | Multi-threshold ultrasonic image segmentation method based on differential search algorithm |
| CN112199996A (en) * | 2020-09-04 | 2021-01-08 | 西安交通大学 | Rolling bearing diagnosis method based on parameter self-adaptive VMD and fast Hoyer spectrogram indexes |
| CN112736912A (en) * | 2020-12-28 | 2021-04-30 | 上海电力大学 | Distribution network reconstruction method based on annealing brownian motion and single ring optimization |
| CN112736912B (en) * | 2020-12-28 | 2023-09-29 | 上海电力大学 | Distribution network reconstruction method based on desuperheating Brownian motion and single-loop optimization |
| CN113722853A (en) * | 2021-08-30 | 2021-11-30 | 河南大学 | Intelligent calculation-oriented group intelligent evolutionary optimization method |
| CN113722853B (en) * | 2021-08-30 | 2024-03-05 | 河南大学 | Group intelligent evolutionary engineering design constraint optimization method for intelligent computation |
| CN117970228A (en) * | 2024-03-28 | 2024-05-03 | 中国人民解放军火箭军工程大学 | Multi-target DOA estimation method based on uniform circular array |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109214499A (en) | A kind of difference searching algorithm improving optimizing strategy | |
| CN110138612B (en) | Cloud software service resource allocation method based on QoS model self-correction | |
| CN108053119B (en) | Improved particle swarm optimization method for solving scheduling problem of zero-waiting line shop | |
| CN103646278B (en) | Application of particle swarm algorithm based on adaptive strategy in robot path planning | |
| CN107316099A (en) | Ammunition Storage Reliability Forecasting Methodology based on particle group optimizing BP neural network | |
| CN108133258A (en) | A kind of mixing global optimization method | |
| CN110442129B (en) | Control method and system for multi-agent formation | |
| CN104408518A (en) | Method of learning and optimizing neural network based on particle swarm optimization algorithm | |
| CN106447024A (en) | Particle swarm improved algorithm based on chaotic backward learning | |
| Manju et al. | An analysis of Q-learning algorithms with strategies of reward function | |
| CN114415663A (en) | Path planning method and system based on deep reinforcement learning | |
| CN113885536B (en) | Mobile robot path planning method based on global gull algorithm | |
| CN107169558A (en) | A kind of Modified particle swarm optimization method for realizing engineering rock mass mechanics parameter inverting | |
| CN112257897B (en) | Electric vehicle charging optimization method and system based on improved multi-target particle swarm | |
| CN113240307B (en) | Power system economic dispatching method based on improved differential evolution algorithm | |
| CN109657800A (en) | Intensified learning model optimization method and device based on parametric noise | |
| CN110163540A (en) | Electric power system transient stability prevention and control method and system | |
| CN113536623A (en) | Topological optimization design method for robustness of material uncertainty structure | |
| CN114330119B (en) | Deep learning-based extraction and storage unit adjusting system identification method | |
| CN114912828A (en) | Optimization method based on numerical control machine tool technological parameters | |
| CN113313322B (en) | MOEA/D extrusion process parameter multi-objective optimization method and device | |
| CN106650028A (en) | Optimization method and system based on agile satellite design parameters | |
| CN117273237A (en) | Method and system for predicting capacity of automobile parts based on artificial intelligence | |
| US20220027708A1 (en) | Arithmetic apparatus, action determination method, and non-transitory computer readable medium storing control program | |
| CN109492744A (en) | A kind of mixed running optimal control method that discrete binary particle swarm algorithm is coupled with fuzzy control |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190115 |