CN114169592A - Transformer station site selection and volume fixing method based on satellite image data analysis - Google Patents
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Abstract
The invention discloses a transformer substation site selection and volume fixing method based on satellite image data analysis, which comprises the following steps: collecting geographic data, electric power system information and economic data, and establishing a database by using the collected data; constructing a mathematical model by using the information of the database; and solving the mathematical model by adopting an ESGA-WVD algorithm, and realizing site selection and volume fixing of the transformer substation according to the solving result of the mathematical model. The global searching capability of the ESGA-WVD algorithm solves the local searching problem of the self-adaptive WVD, further balance of the load rate can be realized on the basis of saving investment, and more excellent results are achieved.
Description
Technical Field
The invention relates to the technical field of planning and design of power systems, in particular to a transformer substation site selection and volume fixing method based on satellite image data analysis.
Background
The transformer substation is a hub of power transmission and distribution, and the planning of the transformer substation determines the operation mode and the power quality of a power distribution network. Therefore, in the early-stage planning of power grid construction, site selection and volume fixing of the transformer substation are the primary links, and the rationality and the economy of the whole planning project are directly determined. In the current research, although there is a research on site selection and volume determination of a transformer substation considering the cost formed by geographic conditions, there is a research on integrating the cost of geographic conditions, the construction and operation cost of the transformer substation and lines and the load rate condition of the transformer substation.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: in the current research, although there is a research on site selection and volume determination of a substation in consideration of costs due to geographical conditions, there is a research on integration of geographical condition costs, substation and line construction operation costs, and load factor of the substation.
In order to solve the technical problems, the invention provides the following technical scheme: collecting geographic data, electric power system information and economic data, and establishing a database by using the collected data; constructing a mathematical model by using the information of the database; and solving the mathematical model by adopting an ESGA-WVD algorithm, and realizing site selection and volume fixing of the transformer substation according to the solving result of the mathematical model.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: the mathematical model includes a set of initial data, a set of constraints, an objective function, and a set of quantities to be solved.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: the constraint conditions include the number of the first and second constraints,
dij≤Ri
wherein, PikRepresenting the load, u (S), connected to substation i by feeder ki) (i ═ 1,2,3 …, k) represents the operating cost of the newly built substation i, SiRepresenting the capacity of substation i, cos phi the load power factor, Ji: set of load points, R, of substation iiIndicates the supply distance limit of substation i, dijIs the euclidean distance between the ith substation and the jth load.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: the objective function includes at least one of,
where ω (i) represents the weight of the distance function designed by the ith substation, xj represents the position vector of the jth load, and mi(i ═ 1,2,3 …, k) denotes the position vector of the ith substation, and e (i) is the sum of substation iAnd (5) fixing the volume.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: the amount to be measured includes mi(i ═ 1,2,3 …, k): a location vector of an ith substation; j. the design is a squarei: and (5) load aggregation in the power supply range of the ith substation.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: solving the mathematical model using the ESGA-WVD algorithm includes, encoding and population generation: the method adopts real number coding and geometric thickness floating point number representation, each floating point number represents an optimization parameter, and a solution needs m-dimensional vector representation x ═ d1,d2,…,dm]Constraining the searched spatial boundaries, each gene of an individual in the population can be represented as:
d(q)＝dl(q)+[du(q)-dl(q)]r
wherein r is a random real number which is more than 0 and less than 1, dl (q) and du (q) are respectively the boundary thickness of each set film, and N individuals meeting the boundary condition are randomly generated to form an initial group; selecting operation: determining the fitness value of the individual, and defining the relationship between the fitness value of the individual and the evaluation function as follows:
V(x)＝-F(x)
employing elite selection mechanism, P percent in the next generation populationsIs generated by direct selection of individuals of (1)% PcIs generated by crossover, P percentmIs generated by mutation, i.e. the current population has a higher fitness value of Ns ═ PsN, N is population size, and individuals are directly copied to the next generation, N in the next generationc＝PcN individuals are generated by crossing parent groups selected and generated according to individual fitness values in the current group, and N ism＝PmN individuals are generated by variation; and (3) cross operation: the algorithm adopts random multipoint uniform cross operation to generate a random integer N1∈[nmin,nv]Wherein n isvNumber of variables for an individual, nminIs a change requiring a crossover operationMinimum number of quantities, selecting nmin＝nv(ii)/2, after determining the number of variables to be crossed, randomly generating a crossing position and performing gene value exchange at the position, generating N from the parent individualsc＝PcN individuals enter the next generation group; mutation operation: randomly generating Nm＝PmN individuals enter the next generation, resulting in the boundary conditions of the individuals changing as the population evolves, the new boundary conditions being given by:
dnew-u(q)＝dold-u(q)-c×[dold-u(q)-μ(q)]
dnew-l(q)＝dold-l(q)-c×[μ(q)-dold-l(q)]
wherein mu (q) is the average value of all individual genes d (q) in the current population, and c is more than 0 and less than 1, which is a control parameter and is selected according to the population scale.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: the data preprocessing of the geographic information database comprises radiometric calibration, atmospheric correction, orthometric correction and image fusion.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: dividing the land types in the geographic information by using a Faster R-CNN neural network, wherein the land types comprise, in an unavailable place: mountains and waters; the high cost makes available: a ground for construction; available ground at low cost: rural areas, cultivated lands, wastelands.
The transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps of: the weight adjustment process comprises the steps of using substation sites as vertexes, constructing a weighted Voronoi diagram according to the weight of each substation, determining a power supply range, and calculating the actual load rate of each substation
t is the current iteration number, i represents the substation; adjusting the actual load rate, if the actual load rate does not accord with the preset condition, the weight value is kept notWhen the weight value is increased, the power supply area of the corresponding transformer substation is reduced, otherwise, the power supply area is enlarged; repeat the iteration according to
And redefining the power supply range of each substation until all the loads borne by the substation meet the load rate requirement.
The invention has the beneficial effects that: the global searching capability of the ESGA-WVD algorithm solves the local searching problem of the self-adaptive WVD, further balance of the load rate can be realized on the basis of saving investment, and more excellent results are achieved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic basic flow chart of a transformer substation site selection and sizing method based on satellite image data analysis according to an embodiment of the present invention;
fig. 2 is a schematic flow diagram of an elite selection genetic algorithm optimization adaptive weighting Voronoi algorithm of a transformer substation site selection and sizing method based on satellite image data analysis according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a result of a transformer substation site selection and sizing method based on satellite image data analysis according to an embodiment of the present invention based on a conventional WVD algorithm;
fig. 4 is a schematic diagram of a result of a transformer substation site selection and sizing method based on satellite image data analysis according to an embodiment of the present invention based on an adaptive WVD algorithm;
fig. 5 is a schematic diagram of a result of an ESGA-WVD algorithm based on a substation siting and sizing method based on satellite image data analysis according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 2, an embodiment of the present invention provides a transformer substation site selection and sizing method based on satellite image data analysis, including:
s1: geographic data, power system information and economic data are collected, and a database is established by utilizing the collected data.
It should be noted that, the data preprocessing is used for eliminating errors caused by a sensor, radiation errors caused by atmosphere and geometric distortion caused by earth rotation when the high-score second satellite acquires data, and fusing multispectral and panchromatic into an image for identification;
the data preprocessing comprises the specific steps of radiometric calibration, atmospheric correction, orthometric correction and image fusion;
the information extraction step firstly extracts land and hydrological information, judges the land to be an unusable land (mountains and water areas), a high-cost usable land (construction land) and a low-cost usable land (rural areas, arable land and wasteland), and judges various information types in the land and hydrological information by using a Faster R-CNN neural network;
the database establishing step is to calculate and predict the construction cost of the transformer substation for various types of land in the unusable land (mountains and water areas), the high-cost usable land (construction land) and the low-cost usable land (fields, cultivated lands and wastelands), wherein the cost of each land type comprises all generalized economic losses caused by destroying original data on the original land type, economic investment for converting the land type into the land type of the constructable transformer substation, economic investment for constructing the transformer substation on the land and generalized economic losses caused by influences on the surrounding environment after the transformer substation is constructed.
S2: and constructing a mathematical model by using the information of the database.
It should be noted that, the mathematical model established according to the data information obtained in S1 includes a set of initial data, a set of constraint conditions, an objective function and a set of quantities to be solved;
the constraint conditions include:
dij≤Ri
wherein, PikRepresenting the load, u (S), connected to substation i by feeder ki) (i ═ 1,2,3 …, k) represents the operating cost of the newly built substation i, SiRepresenting the capacity of substation i, cos phi the load power factor, Ji: set of load points, R, of substation iiIndicates the supply distance limit of substation i, dijIs the euclidean distance between the ith substation and the jth load;
the objective function includes:
where ω (i) represents the weight of the distance function designed by the ith substation, xj represents the position vector of the jth load, and mi(i ═ 1,2,3 …, k) represents the location vector for the ith substation, and e (i) is the rated capacity of substation i;
the waiting quantity comprises:
mi(i ═ 1,2,3 …, k): a location vector of an ith substation;
Ji: supply to the ith substationLoad collection within the electrical range.
When the position and the capacity of a position-changing transformer substation are determined, the initial weight cannot meet the requirement of the actual load rate of the transformer substation, and the load rate of the same transformer substation is always too high or too low, which causes unreasonable power supply range division, the invention adjusts the weight by using the rule shown in table 1 to meet the actual load rate requirement of each transformer substation, and the adjustment process is as follows: using the substation sites as vertices, constructing a weighted Voronoi graph according to the weight of each substation to determine the power supply range, and calculating the actual load rate of each substation
(t is the current iteration number, i represents the substation); the actual load factor is adjusted according to table 1, where a and b represent the upper and lower limits of the actual load factor, a represents the weight adjustment amount,
representing the weight value of the transformer substation i after iteration, if the actual load rate does not accord with the conditions shown in the table 1, keeping the weight value unchanged, and when the weight value is increased, reducing the power supply area of the corresponding transformer substation, otherwise, expanding the power supply area; repeat the iteration according to
Redefining the power supply range of each transformer substation until all loads borne by the transformer substations meet the load rate requirement;
table 1: and (5) adapting the weight to the rule.
The elite selection genetic algorithm is a heuristic global optimization algorithm based on cooperation and competition among individuals, the elite selection genetic algorithm and the adaptive weighting Voronoi graph method are combined for transformer substation planning, the local search problem of the adaptive weighting Voronoi graph method is solved, and therefore extreme points can be skipped to find out a global optimal solution.
S3: and solving the mathematical model by adopting an ESGA-WVD algorithm, and realizing site selection and volume fixing of the transformer substation according to the solving result of the mathematical model.
It should be noted that the use of the ESGA genetic algorithm includes the generation of codes and populations: using real number encoding, the geometric thickness is represented by floating point numbers, each floating point number represents an optimization parameter, a solution requires m-dimensional vector representation x ═ d1, d2, …, dm ], and the space boundary of the search is constrained, and each gene of an individual in the population can be represented by a floating point number:
d(q)＝dl(q)+[du(q)-dl(q)]r
wherein r is a random real number which is more than 0 and less than 1, dl (q) and du (q) are respectively the boundary thickness of each set film, and N individuals meeting the boundary condition are randomly generated to form an initial group;
selecting operation: determining the fitness value of the individual, and defining the relationship between the fitness value of the individual and the evaluation function as follows:
V(x)＝-F(x)
employing elite selection mechanism, P percent in the next generation populationsIs generated by direct selection of individuals of (1)% PcIs generated by crossover, P percentmIs generated by mutation, i.e. N has a higher fitness value in the current populations＝PsN, N is population size, and individuals are directly copied to the next generation, N in the next generationc＝PcN individuals are generated by crossing parent groups selected and generated according to individual fitness values in the current group, and N ism＝PmN individuals are generated by variation; and (3) cross operation: the algorithm adopts random multipoint uniform cross operation to generate a random integer N1∈[nmin,nv]Wherein n isvNumber of variables for an individual, nminIs the minimum number of variables required to be subjected to cross operation, and n is selectedmin＝nv(ii)/2, after determining the number of variables to be crossed, randomly generating a crossing position and performing gene value exchange at the position, generating N from the parent individualsc＝PcN individuals enter the next generation group; mutation operation: followed byMechanically generating Nm＝PmN individuals enter the next generation, resulting in the boundary conditions of the individuals changing as the population evolves, the new boundary conditions being given by:
dnew-u(q)＝dold-u(q)-c×[dold-u(q)-μ(q)]
dnew-l(q)＝dold-l(q)-c×[μ(q)-dold-l(q)]
wherein mu (q) is the average value of all individual genes d (q) in the current population, and c is more than 0 and less than 1, which is a control parameter and is selected according to the population scale.
The method comprises the steps of collecting geographic data, electric power system information and economic data, and establishing a database by utilizing the collected data; constructing a mathematical model by using the information of the database; the ESGA-WVD algorithm is adopted to solve the mathematical model, the global searching capability of the ESGA-WVD algorithm solves the local searching problem of the self-adaptive WVD, and the load rate can be further balanced on the basis of saving investment to realize site selection and volume fixing of the transformer substation according to the solving result of the mathematical model.
Example 2
Referring to fig. 3 to 5, another embodiment of the present invention is different from the first embodiment in that a verification test of a transformer substation site selection and sizing method based on satellite image data analysis is provided, and to verify and explain technical effects adopted in the method, the embodiment adopts a conventional technical scheme and the method of the present invention to perform a comparison test, and compares test results by means of scientific demonstration to verify a real effect of the method.
On the basis of unchanged substation capacity, the power supply range of each substation is adjusted, the balance of planning is further guaranteed with the aim of reducing load difference, when the number and capacity of newly-built substations are determined, the planning results are not completely the same, under normal conditions, the load rate is that the upper limit values of two main transformers are 0.65, the upper limit values of three transformers are 0.87, a load model of a certain floor area 63.08km2 is used, the load model comprises 368 load points, the total load is predicted to be 744.5MW in load saturation years, the power factor is 0.9, substations with 2 × 40, 2 × 50, 3 × 40 and 3 × 50MVA4 capacity specifications are selected as alternative substations, the maximum power supply radius is 3km, table 2 shows the load rate comparison of three algorithms, and the results are shown in the following table:
table 2: load ratio comparison table.
The cost of training the ESGA-WVD algorithm is obviously lower than that of the self-adaptive WVD algorithm and the traditional WVD algorithm, the load distribution area or the actual load rate of the transformer substation is superior to that of the self-adjusting WVD algorithm, although the self-adaptive WVD algorithm has good calculation stability, the defects of the traditional method in the aspects of power supply range division of the transformer substation, uncontrollable load rate and the like are overcome, and the calculation results of different initial points are different. The adaptive WVD algorithm is sensitive to the initial position and is therefore only a local optimization method. In order to overcome insensitivity of the self-adaptive WVD to an initial site, the ESGA-WVD is introduced into a transformer substation planning problem, and example test results show that the global search capability of the ESGA-WVD solves the local search problem of the self-adaptive WVD. ESGA-WVD is a global search optimization algorithm. Finally, the pre-investment of the WVD algorithm is 1.126 million yuan, the pre-investment of the ESGA-WVD is 1.124 million yuan, the further balance of the load rate is realized on the basis of saving the investment, and the result is more excellent.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (9)
1. A transformer substation site selection and volume fixing method based on satellite image data analysis is characterized by comprising the following steps:
collecting geographic data, electric power system information and economic data, and establishing a database by using the collected data;
constructing a mathematical model by using the information of the database;
and solving the mathematical model by adopting an ESGA-WVD algorithm, and realizing site selection and volume fixing of the transformer substation according to the solving result of the mathematical model.
2. The satellite image data analysis-based substation site selection and sizing method according to claim 1, characterized in that: the mathematical model includes a set of initial data, a set of constraints, an objective function, and a set of quantities to be solved.
3. The satellite image data analysis-based substation site selection and sizing method according to claim 2, characterized in that: the constraint conditions include the number of the first and second constraints,
dij≤Ri
wherein, PikRepresenting the load, u (S), connected to substation i by feeder ki) (i ═ 1,2,3 …, k) represents the operating cost of the newly built substation i, SiRepresenting the capacity of substation i, cos phi the load power factor, Ji: set of load points, R, of substation iiIndicates the supply distance limit of substation i, dijIs the euclidean distance between the ith substation and the jth load.
4. The satellite image data analysis-based substation site selection and sizing method according to claim 2, characterized in that: the objective function includes at least one of,
where ω (i) represents the weight of the distance function designed by the ith substation, xj represents the position vector of the jth load, and mi(i ═ 1,2,3 …, k) denotes the location vector of the i-th substation, and e (i) is the rated capacity of substation i.
5. The satellite image data analysis-based substation site selection and sizing method according to claim 2, characterized in that: the amount to be requested may include,
mi(i ═ 1,2,3 …, k): a location vector of an ith substation;
Ji: and (5) load aggregation in the power supply range of the ith substation.
6. The satellite image data analysis-based substation site selection and sizing method according to any one of claims 1 to 5, characterized in that: solving the mathematical model using the ESGA-WVD algorithm includes,
encoding and population generation: using real number encoding, the geometric thickness is represented by floating point numbers, each floating point number represents an optimization parameter, a solution requires m-dimensional vector representation x ═ d1, d2, …, dm ], and the space boundary of the search is constrained, and each gene of an individual in the population can be represented by a floating point number:
d(q)＝dl(q)+[du(q)-dl(q)]r
wherein r is a random real number which is more than 0 and less than 1, dl (q) and du (q) are respectively the boundary thickness of each set film, and N individuals meeting the boundary condition are randomly generated to form an initial group;
selecting operation: determining the fitness value of the individual, and defining the relationship between the fitness value of the individual and the evaluation function as follows:
V(x)＝-F(x)
employing elite selection mechanism, P percent in the next generation populationsIs generated by direct selection of individuals of (1)% PcIs generated by crossover, P percentmIs generated by mutation, i.e. N has a higher fitness value in the current populations＝PsN, N is a group ruleModulo, the individual is directly copied to the next generation, N in the next generationc＝PcN individuals are generated by crossing parent groups selected and generated according to individual fitness values in the current group, and N ism＝PmN individuals are generated by variation;
and (3) cross operation: the algorithm adopts random multipoint uniform cross operation to generate a random integer N1∈[nmin,nv]Wherein n isvNumber of variables for an individual, nminIs the minimum number of variables required to be subjected to cross operation, and n is selectedmin＝nv(ii)/2, after determining the number of variables to be crossed, randomly generating a crossing position and performing gene value exchange at the position, generating N from the parent individualsc＝PcN individuals enter the next generation group;
mutation operation: randomly generating Nm＝PmN individuals enter the next generation, resulting in the boundary conditions of the individuals changing as the population evolves, the new boundary conditions being given by:
dnew-u(q)＝dold-u(q)-c×[dold-u(q)-μ(q)]
dnew-l(q)＝dold-l(q)-c×[μ(q)-dold-l(q)]
wherein mu (q) is the average value of all individual genes d (q) in the current population, and c is more than 0 and less than 1, which is a control parameter and is selected according to the population scale.
7. The satellite image data analysis-based substation site selection and sizing method according to claim 1, characterized in that: the data preprocessing of the geographic information database comprises radiometric calibration, atmospheric correction, orthometric correction and image fusion.
8. The transformer substation site selection and sizing method based on satellite image data analysis according to claim 1 or 7, characterized in that: dividing the land type in the geographic information by using a Faster R-CNN neural network, wherein the land type comprises,
unavoidably: mountains and waters;
the high cost makes available: a ground for construction;
available ground at low cost: rural areas, cultivated lands, wastelands.
9. The satellite image data analysis-based substation site selection and sizing method according to claim 2, characterized in that: the weight adjustment process includes the steps of,
using the substation sites as vertexes, constructing a weighted Voronoi diagram according to the weight of each substation, determining the power supply range, and calculating the actual load rate of each substation
t is the current iteration number, i represents the substation;
adjusting the actual load rate, if the actual load rate does not accord with the preset condition, keeping the weight unchanged, and when the weight value is increased, reducing the power supply area of the corresponding transformer substation, otherwise, expanding the power supply area;
repeat the iteration according to
And redefining the power supply range of each substation until all the loads borne by the substation meet the load rate requirement.
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| CN110619454A (en) * | 2019-08-09 | 2019-12-27 | 东北大学 | Power distribution network planning method based on improved genetic algorithm and PRIM algorithm |
| CN112184028A (en) * | 2020-09-29 | 2021-01-05 | 国家电网有限公司 | Substation engineering dynamic site selection planning method based on harmony search algorithm |
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| CN110619454A (en) * | 2019-08-09 | 2019-12-27 | 东北大学 | Power distribution network planning method based on improved genetic algorithm and PRIM algorithm |
| CN112184028A (en) * | 2020-09-29 | 2021-01-05 | 国家电网有限公司 | Substation engineering dynamic site selection planning method based on harmony search algorithm |
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