CN104573820A - Genetic algorithm for solving project optimization problem under constraint condition - Google Patents
Genetic algorithm for solving project optimization problem under constraint condition Download PDFInfo
- Publication number
- CN104573820A CN104573820A CN201410854641.3A CN201410854641A CN104573820A CN 104573820 A CN104573820 A CN 104573820A CN 201410854641 A CN201410854641 A CN 201410854641A CN 104573820 A CN104573820 A CN 104573820A
- Authority
- CN
- China
- Prior art keywords
- individuality
- genetic algorithm
- algorithm
- optimization
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention discloses a genetic algorithm for solving the project optimization problem under constraint condition. The genetic algorithm comprises the steps of determining an optimization objective of the project optimization problem and establishing an objective function according to the optimization objective; setting ranges of parameters and variables, wherein the parameters include population size, crossing-over rate and mutation probability; randomly producing N individuals and determining initial parameters of the algorithm; determining the value range of n variables according to the constraint condition of the actual problem, evaluating the N individuals and calculating a fitness value and a punishment value; sorting all individuals, firstly adopting elitist strategy selection filial generations and generating residual filial generations through a roulette; traversing each individual, determining the crossing-over digits of each individual and performing crossing-over operation; traversing each individual and conducting mutation operation on the individuals meeting the mutation condition; checking whether the algorithm is completed or not, outputting a result if the algorithm is completed and otherwise, continuing to perform calculation. The probability that the algorithm runs into partial optimization can be lower through improvement.
Description
Technical field
The present invention relates to Genetic Algorithm Technology field, particularly relate to a kind of genetic algorithm of the engineering optimization for Problem with Some Constrained Conditions.
Background technology
Have a class subproblem to be engineering optimization in constrained optimization problem, to be research engineering structure meeting under constraint condition by the optimal design that intended target (as the lightest in weight, cost is minimum) is obtained for it.First conduct a research in aeronautical engineering the sixties in 20th century and apply, being generalized to the engineerings such as machinery, shipbuilding, civil engineering afterwards.
An important feature of engineering optimization is that constraint condition is many and complicated, relates to the restriction relation between parameter, make algorithm in search procedure in condition, consider parameter at space search precision and range.Our genetic algorithm had both considered the precision of convergence, also considered the stability of convergence, guaranteed there is higher robustness.According to the consideration of these two aspects, determine actual algorithm parameter.
With the representative genetic algorithm of heuristic search algorithm, for solving-optimizing problem provides a pattern, it does not rely on particular problem, has extremely strong robustness.The base unit of algorithm is gene chromosome, constitutes population by numerous chromosome.Population at individual is actually gene chromosome coding composition, in whole feasible zone, constantly carry out evolutionary search.In population, each individuality is give the value that represents its search capability by people, is called fitness value (Fitness), and represent the ability that population at individual conforms, fitness is higher, and adaptive faculty is stronger.
SGA＝(C,E,P
0,N,Φ,Γ,Ψ,T)
In formula
C---population at individual coded system.Represent chromogene with scale-of-two, L represents code length, i.e. chromosome length.
E---ideal adaptation degree evaluation function.Represent with f (X)
P
0---initial population
N---population scale, SGA Population Size N represents
Φ---selection opertor, SGA usage ratio operator
Γ---crossover operator, SGA uses single-point to intersect, and probability is P
c
Ψ---mutation operator, SGA uses position evenly to make a variation, and probability is P
m
In population each individual by selection, intersect, mutation operator, repeatedly develop, larger for adaptable individuality probability is all extended to the next generation according to survival of the fittest rule by each iteration end.Best individuality in last population will export through decoding in generation, an optimum solution of this algorithm the most, but not represent must be the optimum solution of this problem.
Herein based on genetic algorithm, propose a kind of new genetic algorithm, for concrete optimization problem, design concrete variable, objective function, constraint condition, algorithm is applied in the middle of actual engineering design, and obtain good effect.
Summary of the invention
The technical problem to be solved in the present invention is for defect of the prior art, provides a kind of genetic algorithm of the engineering optimization for Problem with Some Constrained Conditions.Algorithm of the present invention is by improving, and make the less probability of algorithm be absorbed in local optimum, emulation experiment shows, this algorithm obtains good effect for problems of engineering design in stability, convergence precision.
The technical solution adopted for the present invention to solve the technical problems is: a kind of genetic algorithm of the engineering optimization for Problem with Some Constrained Conditions, comprises the following steps:
1) determine the optimization aim of engineering optimization, set up objective function according to optimization aim; Constrained for band problem is turned to as follows:
minL(X)＝f(X)+(1+|f(X)|)
α(Σ|max(0,g
j(X))|+Σ|h
i(X)|)
s.t.X
L≤X≤X
U
Wherein α represents penalty factor, Σ | max (0, g
j(X) |, Σ | h
i(X) | be penalty value; F (X) is fitness value;
2) parameters and range of variables, described parameter comprises Population Size, crossing-over rate, mutation probability; Random generation individuality, determines the initial parameter of algorithm; The span of n variable is determined according to the constraint condition of practical problems;
3) individuality is assessed, calculates fitness value f (X) and penalty value Σ | max (0, g
j(X)) |, Σ | h
i(X) |;
4) to all individuality sequences, first adopt elitism strategy chooser generation, residue filial generation is generated by roulette;
5) travel through each individuality, determine the intersection figure place of each individuality, carry out interlace operation;
6) travel through each individuality, mutation operation is carried out to the individuality meeting variation condition;
7) check whether algorithm terminates, as terminated then Output rusults; As otherwise return step 3).
By such scheme, when described genetic algorithm parameter is arranged, adopt the method for real number value coding to carry out genetic coding, each gene directly represents a variable.
By such scheme, described step 4) be specially: all individualities are sorted, before choosing
individuality is as elite; The penalty function value individual to residue is normalized, and adopts roulette method to select
individuality.
By such scheme, described step 5) adopt multiple-spot detection, the hop count scope of intersection rounds generation at random in [n/3, n/2], and wherein n represents the number of variable.
By such scheme, described step 6) strategy taked of mutation operation is: allows population searching for as much as possible on a large scale in early days of evolving, searches near current disaggregation in the late period of evolving as far as possible.
The beneficial effect that the present invention produces is:
1. the present invention improves genetic algorithm in conjunction with the optimizing thought of algorithms of different, forms a kind of Hybrid Genetic Algorithm to improve the operational efficiency of genetic algorithm and to solve quality.
2. all multiparameters in genetic algorithm can be directly linked to final search efficiency and result, and effect is revised corresponding parameter and can be improved convergence effect by experiment.
3. increasing progressively with iterations, the optimum solution that obtains of developing close to 100% at the number percent of whole population, means that Search Results is being restrained gradually, the defect individual of previous generation is directly saved to the next generation, convergence speedup speed.
4. the process of continuous iterative search in the scope of whole feasible zone, it is fundamentally the survival probability owing to adding current better individuality after each iteration, by design mutation operation, deliberately change individual search range and search depth, to reach the effect of searching for whole solution space.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described, in accompanying drawing:
Fig. 1 is the method flow diagram of the embodiment of the present invention.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with embodiment, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.
As shown in Figure 1, a kind of genetic algorithm of the engineering optimization for Problem with Some Constrained Conditions, comprises the following steps:
1) determine the optimization aim of engineering optimization, set up objective function according to optimization aim; Constrained for band problem is turned to as follows:
minL(X)＝f(X)+(1+|f(X)|)
α(Σ|max(0,g
j(X))|+Σ|h
i(X)|)
s.t.X
L≤X≤X
U
Wherein α represents penalty factor, Σ | max (0, g
j(X)) |, Σ | h
i(X) | be penalty value; F (X) is fitness value;
2) parameters and range of variables, described parameter comprises Population Size, crossing-over rate, mutation probability; Random generation individuality, determines the initial parameter of algorithm; The span of n variable is determined according to the constraint condition of practical problems;
3) individuality is assessed, calculates fitness value f (X) and penalty value Σ | max (0, g
j(X)) |, Σ | h
i(X) |;
4) to all individuality sequences, first adopt elitism strategy chooser generation, residue filial generation is generated by roulette;
Be specially: all individualities are sorted, before choosing
individuality is as elite; The penalty function value individual to residue is normalized, and adopts roulette method to select
individuality.
5) travel through each individuality, determine the intersection figure place of each individuality, carry out interlace operation; Arranging of interlace operation is determined according to particular problem, if the parametric variable of problem is more, finds out that the association between variable is more by constraint condition, suggestion adopts multiple-spot detection, the hop count scope of intersecting rounds generation at random in [n/3, n/2], and n represents the number of variable.
6) travel through each individuality, mutation operation is carried out to the individuality meeting variation condition; The strategy that mutation operation is taked is: allow population searching for as much as possible on a large scale in early days of evolving, search in the late period of evolving as far as possible near current disaggregation.
7) check whether algorithm terminates, as terminated then Output rusults; As otherwise return step 3).
Should be understood that, for those of ordinary skills, can be improved according to the above description or convert, and all these improve and convert the protection domain that all should belong to claims of the present invention.
Claims (5)
1. for a genetic algorithm for the engineering optimization of Problem with Some Constrained Conditions, it is characterized in that, comprise the following steps:
1) determine the optimization aim of engineering optimization, set up objective function according to optimization aim; Constrained for band problem is turned to as follows:
min L(X)＝f(X)+(1+|f(X)|)
α(∑|max(0，g
j(X))|+∑|h
i(X)|)
s.t.X
L≤X≤X
U
Wherein α represents penalty factor, ∑ | max (0, g
j(X)) |, ∑ | h
i(X) | be penalty value; F (X) is fitness value, and L (X) is penalty function;
2) parameters and range of variables, described parameter comprises Population Size, crossing-over rate, mutation probability; Random generation individuality, determines the initial parameter of algorithm; The span of n variable is determined according to the constraint condition of practical problems;
3) individuality is assessed, calculates fitness value f (X) and penalty value ∑ | max (0, g
j(X)) |, ∑ | h
i(X) |;
4) to all individuality sequences, first adopt elitism strategy chooser generation, residue filial generation is generated by roulette;
5) travel through each individuality, determine the intersection figure place of each individuality, carry out interlace operation;
6) travel through each individuality, mutation operation is carried out to the individuality meeting variation condition;
7) check whether algorithm terminates, as terminated then Output rusults; As otherwise return step 3).
2. genetic algorithm according to claim 1, is characterized in that, when described genetic algorithm parameter is arranged, adopt the method for real number value coding to carry out genetic coding, each gene directly represents a variable.
3. genetic algorithm according to claim 1, is characterized in that, described step 4) be specially: all individualities are sorted, before choosing
individuality is as elite; The penalty function value individual to residue is normalized, and adopts roulette method to select
individuality.
4. genetic algorithm according to claim 1, is characterized in that, described step 5) adopt multiple-spot detection, the hop count scope of intersection rounds generation at random in [n/3, n/2], and wherein n represents the number of variable.
5. genetic algorithm according to claim 1, is characterized in that, described step 6) strategy taked of mutation operation is: allows population searching for as much as possible on a large scale in early days of evolving, searches near current disaggregation in the late period of evolving as far as possible.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410854641.3A CN104573820A (en) | 2014-12-31 | 2014-12-31 | Genetic algorithm for solving project optimization problem under constraint condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410854641.3A CN104573820A (en) | 2014-12-31 | 2014-12-31 | Genetic algorithm for solving project optimization problem under constraint condition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104573820A true CN104573820A (en) | 2015-04-29 |
Family
ID=53089837
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410854641.3A Pending CN104573820A (en) | 2014-12-31 | 2014-12-31 | Genetic algorithm for solving project optimization problem under constraint condition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104573820A (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488568A (en) * | 2015-11-30 | 2016-04-13 | 清华大学 | Meme evolution multiobjective optimization scheduling method based on objective importance decomposition |
| CN105913326A (en) * | 2016-04-06 | 2016-08-31 | 南京农业大学 | Constraining knowledge and elite individual strategy genetic algorithm fusion-based crop growth period model variety parameter optimization method |
| CN106919979A (en) * | 2015-12-25 | 2017-07-04 | 济南大学 | A kind of method that utilization improved adaptive GA-IAGA realizes cotton processing Based Intelligent Control |
| CN106945059A (en) * | 2017-03-27 | 2017-07-14 | 中国地质大学（武汉） | A kind of gesture tracking method based on population random disorder multi-objective genetic algorithm |
| CN108733894A (en) * | 2018-04-26 | 2018-11-02 | 南京航空航天大学 | 3D printing paddle blade structure optimization design based on ANSYS and genetic algorithm |
| CN109343966A (en) * | 2018-11-01 | 2019-02-15 | 西北工业大学 | A kind of cluster organization method and device of unmanned node |
| CN110020805A (en) * | 2019-04-11 | 2019-07-16 | 广东工业大学 | A kind of task calendar formulating method, device and medium |
| CN110348575A (en) * | 2019-06-24 | 2019-10-18 | 南京天洑软件有限公司 | A kind of impeller reverse engineering approach based on genetic algorithm |
| CN110765531A (en) * | 2019-10-23 | 2020-02-07 | 宁波工程学院 | Method for rapidly determining frame rigidity in frame-swinging wall structure |
| CN111126707A (en) * | 2019-12-26 | 2020-05-08 | 华自科技股份有限公司 | Energy consumption equation construction and energy consumption prediction method and device |
| CN111353604A (en) * | 2018-12-24 | 2020-06-30 | 南京理工大学 | Flexible assembly multi-objective dynamic optimization method |
| CN111476497A (en) * | 2020-04-15 | 2020-07-31 | 浙江天泓波控电子科技有限公司 | Feed network distribution method for miniaturized platform |
| CN111724870A (en) * | 2020-06-18 | 2020-09-29 | 成都佳驰电子科技有限公司 | Low-frequency multilayer wave-absorbing material design method based on genetic algorithm |
| CN111967505A (en) * | 2020-07-31 | 2020-11-20 | 武汉市教云慧智信息技术有限公司 | Parameter tuning method of random forest model for smart marketing |
| CN113378276A (en) * | 2021-06-18 | 2021-09-10 | 北方工业大学 | Composite foundation intelligent design method based on genetic algorithm and gene expression programming |
| CN113807015A (en) * | 2021-09-17 | 2021-12-17 | 南方电网科学研究院有限责任公司 | Parameter optimization method, device, equipment and storage medium for compressed air energy storage system |
| CN114417685A (en) * | 2022-01-07 | 2022-04-29 | 北京中安智能信息科技有限公司 | Sonar parameter recommendation method under multi-constraint condition |
| CN117669476A (en) * | 2024-01-31 | 2024-03-08 | 成都电科星拓科技有限公司 | Automatic PCB wiring method, medium and device based on genetic algorithm |
| CN117669476B (en) * | 2024-01-31 | 2024-04-26 | 成都电科星拓科技有限公司 | Automatic PCB wiring method, medium and device based on genetic algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100076913A1 (en) * | 2008-09-24 | 2010-03-25 | Nec Laboratories America, Inc. | Finding communities and their evolutions in dynamic social network |
| CN103401626A (en) * | 2013-08-23 | 2013-11-20 | 西安电子科技大学 | Genetic algorithm based cooperative spectrum sensing optimization method |
-
2014
- 2014-12-31 CN CN201410854641.3A patent/CN104573820A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100076913A1 (en) * | 2008-09-24 | 2010-03-25 | Nec Laboratories America, Inc. | Finding communities and their evolutions in dynamic social network |
| CN103401626A (en) * | 2013-08-23 | 2013-11-20 | 西安电子科技大学 | Genetic algorithm based cooperative spectrum sensing optimization method |
Non-Patent Citations (1)
| Title |
|---|
| 孙红俊等: "面向服务的分布式工作流执行站点优化", 《计算机集成制造系统》 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488568A (en) * | 2015-11-30 | 2016-04-13 | 清华大学 | Meme evolution multiobjective optimization scheduling method based on objective importance decomposition |
| CN106919979A (en) * | 2015-12-25 | 2017-07-04 | 济南大学 | A kind of method that utilization improved adaptive GA-IAGA realizes cotton processing Based Intelligent Control |
| CN105913326B (en) * | 2016-04-06 | 2019-08-06 | 南京农业大学 | Constrain the Crop growing stage model Cultivar parameter optimization method of sex knowledge and the tactful Genetic Algorithm Fusion of elite individual |
| CN105913326A (en) * | 2016-04-06 | 2016-08-31 | 南京农业大学 | Constraining knowledge and elite individual strategy genetic algorithm fusion-based crop growth period model variety parameter optimization method |
| CN106945059A (en) * | 2017-03-27 | 2017-07-14 | 中国地质大学（武汉） | A kind of gesture tracking method based on population random disorder multi-objective genetic algorithm |
| CN106945059B (en) * | 2017-03-27 | 2019-11-22 | 中国地质大学（武汉） | A kind of gesture tracking method based on population random disorder multi-objective genetic algorithm |
| CN108733894A (en) * | 2018-04-26 | 2018-11-02 | 南京航空航天大学 | 3D printing paddle blade structure optimization design based on ANSYS and genetic algorithm |
| CN109343966B (en) * | 2018-11-01 | 2023-04-07 | 西北工业大学 | Cluster organization method and device for unmanned nodes |
| CN109343966A (en) * | 2018-11-01 | 2019-02-15 | 西北工业大学 | A kind of cluster organization method and device of unmanned node |
| CN111353604B (en) * | 2018-12-24 | 2022-08-16 | 南京理工大学 | Flexible assembly multi-objective dynamic optimization method |
| CN111353604A (en) * | 2018-12-24 | 2020-06-30 | 南京理工大学 | Flexible assembly multi-objective dynamic optimization method |
| CN110020805A (en) * | 2019-04-11 | 2019-07-16 | 广东工业大学 | A kind of task calendar formulating method, device and medium |
| CN110020805B (en) * | 2019-04-11 | 2023-11-07 | 广东工业大学 | Task schedule making method, device and medium |
| CN110348575A (en) * | 2019-06-24 | 2019-10-18 | 南京天洑软件有限公司 | A kind of impeller reverse engineering approach based on genetic algorithm |
| CN110765531A (en) * | 2019-10-23 | 2020-02-07 | 宁波工程学院 | Method for rapidly determining frame rigidity in frame-swinging wall structure |
| CN111126707A (en) * | 2019-12-26 | 2020-05-08 | 华自科技股份有限公司 | Energy consumption equation construction and energy consumption prediction method and device |
| CN111126707B (en) * | 2019-12-26 | 2023-10-27 | 华自科技股份有限公司 | Energy consumption equation construction and energy consumption prediction method and device |
| CN111476497A (en) * | 2020-04-15 | 2020-07-31 | 浙江天泓波控电子科技有限公司 | Feed network distribution method for miniaturized platform |
| CN111476497B (en) * | 2020-04-15 | 2023-06-16 | 浙江天泓波控电子科技有限公司 | Distribution feed network method for miniaturized platform |
| CN111724870A (en) * | 2020-06-18 | 2020-09-29 | 成都佳驰电子科技有限公司 | Low-frequency multilayer wave-absorbing material design method based on genetic algorithm |
| CN111967505A (en) * | 2020-07-31 | 2020-11-20 | 武汉市教云慧智信息技术有限公司 | Parameter tuning method of random forest model for smart marketing |
| CN113378276B (en) * | 2021-06-18 | 2022-03-04 | 北方工业大学 | Composite foundation intelligent design method based on genetic algorithm and gene expression programming |
| CN113378276A (en) * | 2021-06-18 | 2021-09-10 | 北方工业大学 | Composite foundation intelligent design method based on genetic algorithm and gene expression programming |
| CN113807015A (en) * | 2021-09-17 | 2021-12-17 | 南方电网科学研究院有限责任公司 | Parameter optimization method, device, equipment and storage medium for compressed air energy storage system |
| CN113807015B (en) * | 2021-09-17 | 2023-12-26 | 南方电网科学研究院有限责任公司 | Parameter optimization method, device, equipment and storage medium for compressed air energy storage system |
| CN114417685A (en) * | 2022-01-07 | 2022-04-29 | 北京中安智能信息科技有限公司 | Sonar parameter recommendation method under multi-constraint condition |
| CN117669476A (en) * | 2024-01-31 | 2024-03-08 | 成都电科星拓科技有限公司 | Automatic PCB wiring method, medium and device based on genetic algorithm |
| CN117669476B (en) * | 2024-01-31 | 2024-04-26 | 成都电科星拓科技有限公司 | Automatic PCB wiring method, medium and device based on genetic algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104573820A (en) | Genetic algorithm for solving project optimization problem under constraint condition | |
| Wang et al. | Modeling carbon emission trajectory of China, US and India | |
| CN112035116B (en) | Agent modeling method for multi-target compiling optimization sequence selection | |
| CN107229972A (en) | A kind of global optimization based on Lamarch inheritance of acquired characters principle, search and machine learning method | |
| CN105303252A (en) | Multi-stage nerve network model training method based on genetic algorithm | |
| CN103971162A (en) | Method for improving BP (back propagation) neutral network and based on genetic algorithm | |
| CN103973221B (en) | A kind of photovoltaic array parameter identification method based on measured data | |
| CN102708047B (en) | Data flow test case generating method | |
| CN105608295A (en) | Multi-objective evolutionary algorithm (MOEA) and radial basis function (RBF) neural network optimization modeling method of coking furnace pressure | |
| CN104200096A (en) | Lightning arrester grading ring optimization method based on differential evolutionary algorithm and BP neural network | |
| Wang et al. | Modified algorithms for fast construction of optimal Latin-hypercube design | |
| CN113971517A (en) | GA-LM-BP neural network-based water quality evaluation method | |
| CN109284818A (en) | A kind of blasting vibration control prediction technique based on accident tree and genetic algorithm | |
| CN106919504B (en) | Test data evolution generation method based on GA algorithm | |
| CN104732067A (en) | Industrial process modeling forecasting method oriented at flow object | |
| CN102521654B (en) | Supercritical water oxidation reaction kinetic model parameter estimation method employing RNA (Ribonucleic Acid) genetic algorithm | |
| CN111126560A (en) | Method for optimizing BP neural network based on cloud genetic algorithm | |
| CN108830292A (en) | Data classification model optimization method and classification method | |
| CN107577918A (en) | The recognition methods of CpG islands, device based on genetic algorithm and hidden Markov model | |
| CN106156854A (en) | A kind of support vector machine parameter prediction method based on DNA encoding | |
| CN110633868A (en) | Method for predicting properties of oil and gas in exploration well oil testing layer by optimizing neural network through genetic algorithm | |
| CN107894710A (en) | A kind of principal component analysis modeling method of cracking reaction furnace temperature | |
| Ferraro et al. | Use of evolutionary algorithms in single and multi-objective optimization techniques for assisted history matching | |
| CN112800673A (en) | Method for predicting blasting block degree based on SA-GA-BP algorithm | |
| CN113743003B (en) | Method for calculating intensity of high-voltage line to ground electric field by considering influence of temperature and humidity |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into 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: 20150429 |