CN102708406A - Scheduling graph optimizing method based on multi-target genetic algorithm - Google Patents
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Abstract
The invention relates to a scheduling graph optimizing method based on a multi-target genetic algorithm. The scheduling graph optimizing method comprises the steps of: scheduling graph simulation model, scheduling graph generalization, the realization form of the multi-target genetic algorithm NSGA-II (nondominated sorting genetic algorithm II): NSGA-II (nondominated sorting genetic algorithm II) algorithm, the generation of an initial population, and the cross heteromorphosis method. In the scheduling graph optimization, the scheduling graph optimizing method adopts the multi-target genetic algorithms, such as the NSGA-II algorithm. The NSGA-II algorithm is known as the algorithm having the best multi-target optimizing effect. The NSGA-II algorithm adopts a rapid domination-free stratified sorting and eliminating mechanism, and introduces in an elite retention strategy, so that the diversity of the results can be ensured so as to make the results widely and uniformly adjacent to the optimal leading edge of Pareto. The multi-target genetic algorithm is relatively nature and stable, which can present stronger optimizing ability no matter in the theoretical test function or the actual production problem. The multi-target genetic algorithm does not need to coordinate a plurality of targets, and moreover, the multi-target genetic algorithm can directly search the non-inferior solutions and provide a mixed coding method. The multi-target genetic algorithm has wide application and strong expandability.
Description
Technical field
The present invention relates to a kind of scheduling graph optimization method based on multi-objective genetic algorithm, is a kind of step reservoir crowd Optimization Dispatching method, relates to a kind of step reservoir crowd scheduling graph Optimization Model structure and method for solving of considering multiple-objection optimization.
Background technology
Conventional scheduling graph formulating method is normally selected a certain typical case year (or typical case's series); Calculate through the runoff adjusting; Can fully merge the experience of scheduler administrator during utilization, and because its simple and practical, easy operating becomes present most widely used conventional scheduling mode.Yet the subject matter that adopts scheduling graph to instruct reservoir operation to exist is: consider forecast come water, normal output district scope too big, be difficult to reach some inevitable shortcomings such as global optimum and accurate global optimum; More scholar hopes to use novel model and optimized Algorithm to improve, and then has proposed Optimization Dispatching figure.Graph of reservoir operation optimization is complicated multi-objective optimization question, so scheduling graph optimization only considers that simple targets such as generated energy maximum or water supply maximum are not enough.Therefore, the introducing of multiple goal algorithm is an inexorable trend.
Summary of the invention
In order to overcome prior art problems, the present invention proposes a kind of scheduling graph optimization method based on multi-objective genetic algorithm, described method adopts NSGA-II algorithm to carry out scheduling graph optimization.
The objective of the invention is to realize like this: a kind of scheduling graph optimization method based on multi-objective genetic algorithm is characterized in that the step of said method is following:
Scheduling graph analogy model: set up the graph of reservoir operation analogy model; Constraint based on the reservoir operation model of scheduling graph; Comprise the constraint of reservoir water balance, the constraint of storage capacity bound, the bound of exerting oneself constraint and the constraint of outflow bound, considering also to comprise the constraint of supplying water under the water supply situation;
Scheduling graph is generally changed: set up graph of reservoir operation.Usually graph of reservoir operation is made up of several rule scheduling lines; Every the scheduling line can be described as one group of interconnective line segment; With the flex point of every line segment as decision variable; Promptly adopt the form of hybrid coding, the flex point horizontal ordinate is defined as the training time variable, ordinate is defined as real number type water level variable.
Multi-objective genetic algorithm NSGA-II:
The way of realization of NSGA-II algorithm:
The generation of initial population: at first generate random number
; The parameter bound is set to
, and the individual i of initial population is generated by following formula:
Cross and variation method: at first preferred parent; Adopt the mode of roulette to generate offspring individual, judge that according to parameter bound
evolution of controlled variable is interval again.
The beneficial effect that the present invention produces is: the present invention is in scheduling graph is optimized, and the multi-objective genetic algorithm of employing is like NSGA-II.NSGA-II is one of best algorithm of present multiple-objection optimization effect of generally acknowledging.The quick non-domination layer sorting of described algorithm use with squeeze mechanism, and introduce elite's retention strategy, can guarantee to make the diversity of separating to separate and more extensively approach the optimum forward position of Pareto uniformly.Described algorithm is ripe, sane, no matter for theoretical trial function, or the actual production problem, all show stronger optimizing ability.Described algorithm need not coordinated a plurality of targets, direct search noninferior solution collection, and the hybrid coding mode is provided, versatility and extensibility are stronger.
Description of drawings
Below in conjunction with accompanying drawing and embodiment the present invention is described further.
Fig. 1 is the scheduling graph Optimization Model frame diagram of embodiments of the invention one said multi-objective genetic algorithm;
Fig. 2 is embodiments of the invention one said Optimization Dispatching figure Design Mode synoptic diagram;
Fig. 3 is the Pareto forward position synoptic diagram of the objective function of embodiments of the invention one said instance;
Fig. 4 is level ground, the temple optimizing scheduling of reservoir diagram intention of embodiments of the invention one said instance.
Embodiment
Embodiment one:
Present embodiment is a kind of scheduling graph optimization method based on multi-objective genetic algorithm, it is characterized in that the step of said method is following:
Scheduling graph analogy model: set up the graph of reservoir operation analogy model; Constraint based on the reservoir operation model of scheduling graph; Comprise the constraint of reservoir water balance, the constraint of storage capacity bound, the bound of exerting oneself constraint and the constraint of outflow bound, considering also to comprise the constraint of supplying water under the water supply situation;
Scheduling graph is generally changed: set up graph of reservoir operation.Usually graph of reservoir operation is made up of several rule scheduling lines; Every the scheduling line can be described as one group of interconnective line segment; With the flex point of every line segment as decision variable; Promptly adopt the form of hybrid coding, the flex point horizontal ordinate is defined as the training time variable, ordinate is defined as real number type water level variable.
Multi-objective genetic algorithm NSGA-II:
The way of realization of NSGA-II algorithm:
The generation of initial population: at first generate random number
; The parameter bound is set to
, and the individual i of initial population is generated by following formula:
Cross and variation method: at first preferred parent; Adopt the mode of roulette to generate offspring individual, judge that according to parameter bound
evolution of controlled variable is interval again.
The principle of the said method of present embodiment:
1) based on the scheduling graph analogy model
Reservoir operation model based on scheduling graph is to be guidance with the graph of reservoir operation, is restrained boundary with the reservoir engineering parameter, adopts analogy method to pursue the dispatching simulation of period.This model is the basis of all step reservoir crowd combined dispatching figure Optimization Model.
Draw together the constraint of reservoir water balance, the constraint of storage capacity bound, the bound of exerting oneself constraint and the constraint of outflow bound based on the constraint of the reservoir operation model of scheduling graph.Considering also to comprise the constraint of supplying water under the water supply situation.
2) scheduling graph is generally changed
As shown in Figure 2, graph of reservoir operation is made up of several rule scheduling lines usually, and every scheduling line can be described as one group of interconnective line segment.The flex point of every line segment as decision variable, is promptly adopted the form of hybrid coding, the flex point horizontal ordinate is defined as the training time variable, ordinate is defined as real number type water level variable.If a plurality of flex point water levels of definition are identical when scheduling graph is generally changed, then can reduce the water level variable.For a scheduling graph that 3 scheduling lines are arranged; Suppose that every scheduling line has 4 flex points and 2 water level platforms; Then can this scheduling graph be generalized as the optimization problem of 3 * (4+2)=18 parameters (decision variable), and concerning month by month, ten days mode scheduling graph, the decision variable number is the same.And based on the generalization mode by period pure water position, 3 * 12=36 decision variable of scheduling graph needs pursues 3 * 36=108 decision variable of ten days scheduling graph needs month by month.
Mix generalization mode and can greatly reduce the decision variable number, reduce the scale of Optimization Model, for improving Optimization Model efficient and seeking globally optimal solution and lay a good foundation.
3) multi-objective genetic algorithm NSGA-II
NSGA-II is one of best algorithm of present multiple-objection optimization effect of generally acknowledging.This method adopts quick non-domination layer sorting and squeezes mechanism, and introduces elite's retention strategy, can guarantee to make the diversity of separating to separate and more extensively approach the optimum forward position of Pareto uniformly.Algorithm is ripe, sane, no matter for theoretical trial function, or the actual production problem, all show stronger optimizing ability.Algorithm need not coordinated a plurality of targets, direct search noninferior solution collection, and the hybrid coding mode is provided, versatility and extensibility are stronger.
The way of realization of NSGA-II algorithm:
1, the generation of initial population: at first generate random number
; The parameter bound is set to
, and the individual i of initial population is generated by following formula:
2, cross and variation method: at first preferred parent; Adopt the mode of roulette to generate offspring individual, judge that according to parameter bound
evolution of controlled variable is interval again.
Be below present embodiment in the steep mountain range of Hanjiang River sub-graph of reservoir operation optimized application.
Level ground, temple reservoir is located in Xiao Jia gulf, Si Ping town, Baokang County, Hubei Province, the 5km apart from the Si Ping town, and the 102.4km apart from the river mouth, dam site water catching area 2150 km2, mean annual runoff 9.94 hundred million m3 are positioned on the epimere powder Qinghe, the Nanhe River, right bank tributary, middle reaches, Han River.Engineering is main with generating, has comprehensive utilization benefits such as flood control, irrigation, aquaculture, reservoir area shipping concurrently.
Fig. 3 is the Pareto forward position of scheduling graph Model for Multi-Objective Optimization the average annual energy output maximum that obtains and maximum two targets of fraction of generating electricity.Can two from figure be game between the target, pursue the generated energy maximum and must lose the generating fraction, vice versa, so Pareto forward position curve tilts to the lower right.Two target function values of design scheduling graph also are plotted on the figure, can find out that the Pareto forward position of the optimised some formation of the corresponding design point of design scheduling graph is arranged.
According to the distribution situation in above-mentioned Pareto forward position, select the optimization solution (shown in the table 1) of some domination design scheduling graph simulation points.Can see that from table the generated energy of these optimization solutions and generating fraction are all good than the design scheduling graph.The generated energy of optimization solution 1 will exceed 1245.30 ten thousand kwh than the design scheduling graph, and the additional issue rate has reached 9.05%, and the generating fraction brings up to 74.17%, and the generated energy of optimization solution 6 has improved 5.09% than the design scheduling graph, and the generating fraction brings up to 75.65%.Choose optimization solution 5 as suggested design, wherein generated energy has increased by 719.03 ten thousand kwh, and the additional issue rate has reached 5.23%; The generating fraction brings up to 75.46% than originally, improved 1.48 percentage points, and average generating flow has increased 1.16m3/s than originally; Increment rate reaches 4.85%; Abandon the water yield and reduced by 0.37 hundred million m3, reduce 22.98%, operational effect is better than the design scheduling graph.Fig. 4 is corresponding Optimization Dispatching figure.
The optimization solution of table 1 domination design scheduling graph
| ? | Generated energy | The generating fraction |
| The design scheduling graph | 1.3750 | 73.98 |
| Optimization solution 1 | 1.4996 | 74.17 |
| Optimization solution 2 | 1.4931 | 74.54 |
| Optimization solution 3 | 1.4903 | 74.91 |
| Optimization solution 4 | 1.4483 | 75.19 |
| Optimization solution 5 | 1.4469 | 75.46 |
| Optimization solution 6 | 1.4450 | 75.65 |
Annotate: generated energy (hundred million kwh), generating fraction (%)
Claims (2)
1. scheduling graph optimization method based on multi-objective genetic algorithm is characterized in that the step of said method is following:
Scheduling graph analogy model: set up the graph of reservoir operation analogy model; Constraint based on the reservoir operation model of scheduling graph; Comprise the constraint of reservoir water balance, the constraint of storage capacity bound, the bound of exerting oneself constraint and the constraint of outflow bound, considering also to comprise the constraint of supplying water under the water supply situation;
Scheduling graph is generally changed: set up graph of reservoir operation.
2. graph of reservoir operation is made up of several rule scheduling lines usually; Every the scheduling line can be described as one group of interconnective line segment; With the flex point of every line segment as decision variable; Promptly adopt the form of hybrid coding, the flex point horizontal ordinate is defined as the training time variable, ordinate is defined as real number type water level variable;
Multi-objective genetic algorithm NSGA-II:
The way of realization of NSGA-II algorithm:
The generation of initial population: at first generate random number
; The parameter bound is set to
, and the individual i of initial population is generated by following formula:
Cross and variation method: at first preferred parent; Adopt the mode of roulette to generate offspring individual, judge that according to parameter bound
evolution of controlled variable is interval again.
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Application publication date: 20121003 |