CN108133257B - Pump station optimization method based on artificial fish swarm algorithm - Google Patents
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Abstract
The invention belongs to the technical field of water conservancy information, and particularly relates to a pump station optimization method based on an artificial fish swarm algorithm; the technical problem to be solved is as follows: the artificial fish swarm algorithm-based pump station optimization method can quickly, effectively and accurately carry out pump station optimization scheduling so as to promote efficient utilization of water resources; the technical scheme is as follows: s101, initializing system parameters; s102, initializing a fish school in a variable allowable range, and solving a target function value of each artificial fish; s103, iterative optimization, namely performing clustering behavior and rear-end collision behavior on the artificial fish respectively, comparing the function values of the two behaviors, and selecting the behavior with the larger function value for execution; s104, judging whether the iteration times reach the maximum allowable iteration times, if so, stopping the iteration operation and outputting the maximum function value, otherwise, continuing the iteration; the invention is suitable for the field of water conservancy.
Description
Technical Field
The invention belongs to the technical field of water conservancy information, and particularly relates to a pump station optimization method based on an artificial fish swarm algorithm.
Background
The research on the joint scheduling problem of the hydraulic system pump station in China is always a key problem for enhancing the efficient utilization of water resources and protecting the water environment. For example, the large-scale hydraulic engineering south-to-north water transfer east line engineering in China has been completed and put into operation, and the east line engineering relates to the scheduling problem of a multi-stage pump station. At the present stage, a cascade pump station group of the east line of northeast China-of-south China water is lack of scientific and effective control and management, and the operation of a pump station is controlled roughly mainly by human experience and simple technical indexes, so that the water transfer task cannot be completed accurately, the pump station is in an economically optimized operation state, and resource waste is caused. Optimization studies on pumping stations have become important.
Currently, there have been some achievements on the research on pump station optimization, such as pump station optimization scheduling based on genetic algorithm, ant colony algorithm, particle swarm algorithm, and the like. However, the artificial fish swarm algorithm as an emerging bionic intelligent optimization algorithm is not yet applied to the optimization scheduling of the pump station.
The artificial fish swarm algorithm AFSA is a new random search intelligent optimization algorithm based on simulated fish swarm behavior. The method mainly simulates the behavior characteristics of foraging, herding, rear-end collision and the like of fish schools in life, and achieves the optimization capability of the algorithm by constructing a plurality of single artificial fishes to aim at searching the position with the highest food concentration. The algorithm has a fast convergence speed and does not need to describe the problem accurately. Therefore, the method has wide application range and prospect. However, as they develop and apply, several disadvantages have also been discovered: the blindness of the later stage of the algorithm search is large, and the optimization process of the algorithm becomes complicated and the precision is not high enough due to the fixed view and the step length.
Disclosure of Invention
The invention overcomes the defects of the prior art, and solves the technical problems that: the pump station optimization method based on the artificial fish swarm algorithm can quickly, effectively and accurately carry out pump station optimization scheduling so as to promote efficient utilization of water resources.
In order to solve the technical problems, the invention adopts the technical scheme that: a pump station optimization method based on an artificial fish swarm algorithm comprises the following steps: s101, initializing system parameters, wherein the parameters comprise: visual field range Visual of the fish school algorithm, moving Step length, crowding factor delta and fish school scale N;
s102, changing the state of the artificial fish into X ═ { X ═ X1,x2,...,xnVector representation, xi{ i ═ 1.. times, n } denotes the variables to be optimized, { X1,X2,… ,XNIndicates that the fish shoal is initialized within the allowable range of the variable and is expressed by the formula
Thousand yuan, solving the objective function value of each artificial fish; wherein: p is a radical ofrIs the electricity price, and the unit is: thousand yuan/kWh; rho is water density, rho is1000kg/m3(ii) a g is gravity acceleration, g is 9.80665m/s2；QiThe unit is the pumping flow of the ith pump group: m is3/s；HstiThe unit is the net lift of the pump set of the ith platform: m; etastiThe station efficiency of the ith pump group; s103, iterative optimization, namely performing clustering behavior and rear-end collision behavior on the artificial fish respectively, comparing the function values obtained by the two behaviors, finally selecting the behavior with a larger function value to perform, and recording the optimal iterative value; and S104, judging whether the iteration times reach the maximum allowable iteration times, if so, stopping the iteration operation and outputting the maximum function value, otherwise, turning to the step S103 to continue the iteration.
Preferably, in step S103, the clustering behavior includes the following steps: state is XiSearching for the number n of artificial fish in the neighborhoodfAnd finding out the center position XcIf, if
Wherein: y isiIs XiCorresponding function value, YcIs XcCorresponding function value, indicating XcWith more food and less crowdedness, XiAnd moving one step towards the direction of the central position, otherwise, executing foraging action.
Preferably, in step S103, the rear-end collision behavior includes the following steps: state is XiSearching all artificial fishes in the neighborhood by the corresponding function value YjLargest artificial fish XjIf, if
Wherein: y isiIs XiCorresponding function value, nfThe number of artificial fish in the neighborhood indicates the artificial fish XjWith higher food concentration and less crowding around, the artificial fish is oriented towards XjThe direction is further, otherwise, foraging is performed.
Preferably, the foraging behaviour comprises the steps of: the current state of the known artificial fish is XiThe corresponding function value is YiWhich randomly looks for a state X in the field of viewjAnd judging the function value Y corresponding to the statejWhether it is better than the current function value YiIf yes, the artificial fish is oriented to XjThe direction is advanced by one step; otherwise, randomly searching the next state again; if the advance condition is not satisfied after try _ number is repeatedly tried, randomly moving by one step, wherein: the try number is the maximum number of attempts.
Preferably, the artificial fish performs said foraging action if repeated attempts are made
And the next time, the advancing condition is not met, and the direction of the optimal value point is directly taken to move.
Preferably, if the artificial fish does not satisfy the herding behavior, rear-end behavior and foraging behavior, a state is randomly selected within the visual field of the artificial fish, and then the artificial fish moves towards the state.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention innovatively applies a fish swarm algorithm to pump station optimization, firstly initializes system parameters, calculates a target function value of each artificial fish, then executes a swarm behavior and a rear-end collision behavior on the artificial fish respectively, compares the function values obtained by the two behaviors, and finally selects a behavior with a larger function value to execute; compared with the traditional intelligent optimization algorithm such as a genetic algorithm, the method improves the traditional artificial fish swarm algorithm, so that the visual field and the step length can be dynamically changed, and the step length and the visual field are gradually reduced in the final stage of the algorithm, so that the optimal value can be more accurately found, the optimal scheduling of the pump station can be rapidly, effectively and accurately carried out, and the efficient utilization of water resources is promoted.
2. In the invention, when the artificial fish performs foraging, if repeated attempts are made
The next time, the moving condition is not satisfied, and the direction of the optimal value point is directly taken to move, thus not only enhancing the artificial fishThe foraging capability also avoids the defect caused by the foraging behavior in the traditional fish swarm algorithm occupying a large amount of resources in the system.
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The present invention will be described in further detail with reference to the accompanying drawings;
fig. 1 is a schematic flow chart of a pump station optimization method based on an artificial fish swarm algorithm according to an embodiment of the present invention;
fig. 2 is a diagram of an optimization process for optimizing a pump station by respectively using an artificial fish swarm algorithm and a conventional genetic algorithm provided in the first embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fish living in water have such obvious characteristics: they often appear in groups, even if some fishes fall singly, the fishes can quickly find other fish groups through trailing, foraging and clustering actions, and the larger the fish group is, the higher the food concentration is. The artificial fish swarm algorithm constructs artificial fish to simulate various living behaviors of fish according to the characteristics of the fish swarm to realize optimization. The behavior of fish schools is as follows: foraging behavior, herding behavior, tailgating behavior, and stochastic behavior.
In the present invention, the state vector X of the artificial fish is (X)1,x2,...,xn) Denotes xi(i 1.., n) represents a variable to be optimized; the food concentration of the current position of the artificial fish is as follows: y ═ f (x), Y is the objective function value; the distance between the artificial fish individuals is as follows: di,j＝||Xi-XjL; the perceived distance (i.e., the field of view) of an artificial fish is: visual; maximum movement of artificial fishThe step length is: step; the crowding factor is: δ;
fig. 1 is a schematic flow chart of a pump station optimization method based on an artificial fish swarm algorithm according to an embodiment of the present invention, and as shown in fig. 1, the pump station optimization method based on the artificial fish swarm algorithm includes the following steps:
s101, initializing system parameters, wherein the parameters comprise: visual field range of fish swarm algorithm, moving Step length, crowding factor delta and fish swarm size N.
The field of view and the step size have important influence on the convergence speed and precision of the algorithm and the complexity of the system. The visual field range is large, so that the artificial fish can realize global optimization, but more resources are consumed; the visual field range is small, and the overall optimizing capability of the artificial fish is limited. The step length is large, the algorithm can realize rapid convergence, but the precision is reduced; the opposite is true when the step size is small.
Visual＝Visual×α
Step＝Step×α
Where gen represents the current number of iterations and MAXGEN represents the total number of iterations.
S102, changing the state of the artificial fish into X ═ { X ═ X1,x2,...,xnVector representation, xi{ i ═ 1.. times, n } denotes the variables to be optimized, { X1,X2,… ,XNIndicates that the fish shoal is initialized within the allowable range of the variable and is expressed by the formula
Thousand yuan, and the objective function value of each artificial fish is solved.
Wherein: p is a radical ofrIs the electricity price, and the unit is: thousand yuan/kWh; rho is water density, and rho is 1000kg/m3(ii) a g is gravity acceleration, g is 9.80665m/s2；QiThe unit is the pumping flow of the ith pump group: m is3/s；HstiFor pump groups of i-th stageNet lift, in units of: m; etastiIs the station efficiency of the ith pump stack.
S103, iterative optimization, namely performing clustering behavior and rear-end collision behavior on the artificial fish respectively, comparing the function values obtained by the two behaviors, finally selecting the behavior with a larger function value to perform, and recording the optimal iterative value;
and S104, judging whether the iteration times reach the maximum allowable iteration times, if so, stopping the iteration operation and outputting the maximum function value, otherwise, turning to the step S103 to continue the iteration.
Compared with the traditional intelligent optimization algorithm such as a genetic algorithm, the method improves the traditional artificial fish swarm algorithm, so that the visual field and the step length can be dynamically changed, and the step length and the visual field are gradually reduced in the final stage of the algorithm, so that the optimal value can be more accurately found, the optimal scheduling of the pump station can be rapidly, effectively and accurately carried out, and the efficient utilization of water resources is promoted.
Specifically, in step S103, the clustering behavior includes the following steps:
state is XiSearching for the number n of artificial fish in the neighborhoodfAnd finding out the center position XcIf, if
Wherein: y isiIs XiCorresponding function value, YcIs XcCorresponding function value, indicating XcWith more food and less crowdedness, XiAnd moving one step towards the direction of the central position, otherwise, executing foraging action.
Specifically, in step S103, the rear-end collision behavior includes the following steps:
state is XiSearching all artificial fishes in the neighborhood by the corresponding function value YjLargest artificial fish XjIf, if
Wherein: y isiIs XiCorresponding function value, nfIn the neighborhoodThe number of the artificial fish indicates the number of the artificial fish XjWith higher food concentration and less crowding around, the artificial fish is oriented towards XjThe direction is further, otherwise, foraging is performed.
Specifically, the foraging behavior comprises the following steps:
the current state of the known artificial fish is XiThe corresponding function value is YiWhich randomly looks for a state X in the field of viewjAnd judging the function value Y corresponding to the statejWhether it is better than the current function value YiIf yes, the artificial fish is oriented to XjThe direction is advanced by one step; otherwise, randomly searching the next state again; if the advance condition is not satisfied after try _ number is repeatedly tried, randomly moving by one step, wherein: the try number is the maximum number of attempts.
More specifically, when the artificial fish performs the foraging action, repeated attempts are made
And the next time, the advancing condition is not met, and the direction of the optimal value point is directly taken to move.
When the basic fish swarm algorithm executes foraging operation, points in a visual field are randomly searched, if the points are found to be more optimal than the current position of the basic fish swarm algorithm, the points move to the direction, and the searching times are often smaller than the value of Try _ number. Therefore, the currently found point is not the optimal point in the visual field, so that the foraging capacity of the artificial fish is greatly weakened, and a large amount of resources in the system are occupied. For these characteristics, we consider that the artificial fish directly takes the direction of the optimal value point to move after half of the Try _ number is executed. Therefore, the foraging capability of the artificial fish is enhanced, and the defect that the foraging behavior in the traditional fish swarm algorithm occupies a large amount of resources in the system is avoided.
Specifically, if the artificial fish does not satisfy the herding behavior, rear-end behavior, and foraging behavior, a state is randomly selected within the visual field thereof, and then moved in the direction.
FIG. 2 is a diagram illustrating an artificial fish school calculation according to the first embodiment of the present inventionAn optimization process diagram for optimizing the pump station by a method and a traditional genetic algorithm, wherein the optimization process diagram adopts a six-in-one (M350 HD-10) model mixed-flow pump (one of which is standby), and the total flow Q is 1.5M3S, electricity price pr0.5 yuan/kW.h, single machine flow constraint Qi∈[0.3,0.5]The pump station lift H belongs to [9,14 ]]And the water pump running efficiency eta is more than or equal to 0.80, and the water pump running efficiency eta is obtained by fitting a polyfit function in MATLAB: head flow curve h (Q) ═ -92.4257Q2+32.6487Q +13.9505, efficiency flow curve η (Q) — 8.6176Q2+7.2438Q-0.6679, since the objective function is to solve the minimum, simply take its value negative, then the problem turns into solving the maximum problem. The figure compares the performance of the pump station optimization method based on the genetic algorithm and the improved artificial fish swarm algorithm provided by the invention. As can be seen from FIG. 2, the improved artificial fish swarm algorithm and the genetic algorithm can both obtain the optimal results, but the convergence rate is faster.
On the basis of the existing theory, the basic fish swarm algorithm is improved to a certain extent, so that the visual field and the step length of the basic fish swarm algorithm can be dynamically changed, the basic fish swarm algorithm moves towards the optimal solution direction in the visual field range when foraging is carried out, the convergence speed and the accuracy of the basic fish swarm algorithm are obviously improved, the basic fish swarm algorithm also has a good effect on the aspect of pump station optimization, the basic fish swarm algorithm is superior in performance and easy to realize, and therefore the basic fish swarm algorithm has prominent substantive characteristics and remarkable progress.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A pump station optimization method based on an artificial fish swarm algorithm is characterized in that: the method comprises the following steps:
s101, initializing system parameters, wherein the parameters comprise: visual field range Visual of the fish school algorithm, moving Step length, crowding factor delta and fish school scale N;
s102, changing the state of the artificial fish into X ═ { X ═ X1,x2,...,xnVector representation, xi{ i ═ 1.. times, n } denotes the variables to be optimized, { X1,X2,… ,XNIndicates that the fish shoal is initialized within the allowable range of the variable and is expressed by the formula
Solving the objective function value of each artificial fish;
wherein: p is a radical ofrIs the electricity price, and the unit is: thousand yuan/kWh; rho is water density, and rho is 1000kg/m3(ii) a g is gravity acceleration, g is 9.80665m/s2；QiThe unit is the pumping flow of the ith pump group: m is3/s；HstiThe unit is the net lift of the pump set of the ith platform: m; etastiThe station efficiency of the ith pump group;
s103, iterative optimization, namely performing clustering behavior and rear-end collision behavior on the artificial fish respectively, comparing the function values obtained by the two behaviors, finally selecting the behavior with a larger function value to perform, and recording the optimal iterative value;
and S104, judging whether the iteration times reach the maximum allowable iteration times, if so, stopping the iteration operation and outputting the maximum function value, otherwise, turning to the step S103 to continue the iteration.
2. The pump station optimization method based on the artificial fish swarm algorithm according to claim 1, characterized in that: in step S103, the clustering act includes the following steps:
state is XiSearching for the number n of artificial fish in the neighborhoodfAnd finding out the center position XcIf, if
Wherein: y isiIs XiCorresponding function value, YcIs XcCorresponding function value, indicating XcWith more food and less crowdedness, XiAnd moving one step towards the direction of the central position, otherwise, executing foraging action.
3. The pump station optimization method based on the artificial fish swarm algorithm according to claim 1, characterized in that: in step S103, the rear-end collision behavior includes the following steps:
state is XiSearching all artificial fishes in the neighborhood by the corresponding function value YjLargest artificial fish XjIf, if
Wherein: y isiIs XiCorresponding function value, nfThe number of artificial fish in the neighborhood indicates the artificial fish XjWith higher food concentration and less crowding around, the artificial fish is oriented towards XjThe direction is further, otherwise, foraging is performed.
4. The pump station optimization method based on the artificial fish swarm algorithm according to any one of claims 2 or 3, wherein: the foraging behavior comprises the following steps:
the current state of the known artificial fish is XiThe corresponding function value is YiWhich randomly looks for a state X in the field of viewjAnd judging the function value Y corresponding to the statejWhether it is better than the current function value YiIf yes, the artificial fish is oriented to XjThe direction is advanced by one step; otherwise, randomly searching the next state again; if the advance condition is not satisfied after try _ number is repeatedly tried, randomly moving by one step, wherein: the try number is the maximum number of attempts.
5. The pump station optimization method based on the artificial fish swarm algorithm according to claim 4, wherein the pump station optimization method comprises the following steps: when the artificial fish executes the foraging action, if repeated attempts are made
And the next time, the advancing condition is not met, and the direction of the optimal value point is directly taken to move.
6. The pump station optimization method based on the artificial fish swarm algorithm according to claim 4, wherein the pump station optimization method comprises the following steps: if the artificial fish does not meet the clustering behavior, rear-end collision behavior and foraging behavior, a state is randomly selected in the visual field range of the artificial fish, and then the artificial fish moves towards the state.
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Non-Patent Citations (2)
| Title |
|---|
| A Novel Self-Adaptation Hybrid Artificial Fish-Swarm Algorithm;Hu X.T.等;《6th IFAC Symposium on Mechatronic Systems The International Federation of Automatic Control》;20130412;第583-588页 * |
| 基于改进人工鱼群算法的梯级水库群优化调度;彭勇 等;《系统工程理论与实践》;20110630;第1118-1125页 * |
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