CN115578038A - Electric energy meter logistics scheduling method and system for large-scale batch rotation tasks - Google Patents
Electric energy meter logistics scheduling method and system for large-scale batch rotation tasks Download PDFInfo
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Abstract
The invention discloses an electric energy meter logistics scheduling method and system for large-scale batch rotation tasks, which comprises the following steps: establishing an electric energy meter logistics scheduling model for large-scale batch rotation tasks; constructing a mapping initial distribution population with high ergodicity through cubic chaotic mapping, and setting parameters; calculating the fitness value of each distribution scheme by a basic badger optimization algorithm, and taking the minimum fitness value as an optimal fitness value; calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating a novel distribution scheme; and judging whether the iteration of the algorithm falls into stagnation or not. The method has the advantages of high convergence speed, high precision and strong stability, can effectively improve the logistics dispatching and delivery efficiency of the electric energy meters for large-scale batch alternate tasks, and reduces the transportation cost; in addition, the objective function is clear, the decision variables and the constraint conditions meet the actual conditions, the economic benefit and efficiency of logistics transportation can be effectively improved, and reasonable distribution of large-scale batch rotation logistics scheduling is realized.
Description
Technical Field
The invention relates to the technical field of logistics scheduling, in particular to a large-scale batch rotation task-oriented electric energy meter logistics scheduling method and system.
Background
With the continuous development of digital economy, the entity economy and the digital economy are fused with each other, and the intelligent level of a power grid is obviously improved. The construction of smart grids and smart power has gradually become the main development trend of current grid enterprises. The intelligent logistics is an important component of the optimization and integration of a supply chain of a power grid enterprise, and is a necessary step for the power grid enterprise to move forward to the 5G era comprehensively and promote the deep integration of digital twins and a power grid, so that the requirement for further refinement of the object of a logistics scheduling model is necessarily made when a digital and economical intelligent logistics supply system is constructed.
Compared with the traditional cargo delivery scheduling scheme, the power grid delivery is different, and the power grid delivery has the complex constraints of wide delivery range, large quantity and complex types of delivered cargos, different signed carrying vehicle types, transportation distance and the like, so that the core problem of transregional power grid delivery transportation is the multi-target vehicle path planning problem related to multi-dimensional complex environmental condition constraints.
Because the electric energy meter logistics scheduling oriented to the large-scale batch rotation task has the characteristics of large distribution quantity requirement, cross-region, strict time requirement and the like, an efficient and intelligent electric energy meter logistics scheduling scheme is urgently needed in the current logistics industry, and scheme research and technical design are performed on the premise of the invention.
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 title of the 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 invention is provided in view of the problems that the existing electric energy meter logistics scheduling for large-scale batch rotation tasks has large distribution quantity requirements, cross-region and strict time requirements and the like.
Therefore, the invention aims to provide a logistics scheduling method and a logistics scheduling system of an electric energy meter for large-scale batch rotation tasks.
In order to solve the technical problems, the invention provides the following technical scheme:
the electric energy meter logistics scheduling method for the large-scale batch rotation task, provided by the invention, comprises the following steps of: collecting total transportation time, distribution cost, punishment cost and vehicle idle rate data to establish an electric energy meter logistics scheduling model of large-scale batch rotation tasks;
constructing a mapping initial distribution population with high ergodicity through cubic chaotic mapping, and setting parameters;
calculating the fitness value of each distribution scheme by a basic badger optimization algorithm and taking the minimum fitness value as an optimal fitness value;
calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating a novel distribution scheme;
and judging whether the iteration of the algorithm is stuck to be stopped.
The electric energy meter logistics scheduling method for the large-scale batch rotation task, provided by the invention, comprises the following steps of: the electric energy meter logistics scheduling model of the large-scale batch rotation task comprises,
the objective function is:
therein, sigma A＝1 、∑ i＝1 、∑ j＝1 、∑ k＝1 Respectively the total transportation time, the distribution cost, the punishment cost and the vehicle empty load rate,
the total transportation cost of the A-th vehicle with the model number m in the transportation process,
Is the total time of transport of the vehicle Am,
as total rest time of the vehicle, C Am To penalize cost, W Am The vehicle empty load rate;
all constraints need to be satisfied in the calculation process of the objective function.
The electric energy meter logistics scheduling method for the large-scale batch rotation task, provided by the invention, comprises the following steps of: the constraints include a vehicle delivery point origination constraint, a vehicle loading limit constraint, a maximum sunrise library constraint, a permit time constraint, and a number of vehicles per model constraint.
The electric energy meter logistics scheduling method for the large-scale batch rotation task, provided by the invention, comprises the following steps of: constructing a mapping initial distribution population with high ergodicity by cubic chaotic mapping, and setting parameters comprising
The formula of the cubic chaotic map is as follows:
c(o+1)＝4c(o) 3 -3y(o)-1<c(0)<1，c(o)≠0o＝0，1，2，...
wherein c is a chaotic variable, and an initial melissa population is set to be composed of Nop D-dimensional individuals, so that Nop feasible distribution populations can be initialized;
generating a random number rand between [ -1,1] as the position of the first dimension in each population individual;
generating subsequent D-1 individuals of each dimension in the population individuals by an iterative method, and mapping variable values generated by cubic mapping into the individual population of the badger, wherein the specific formula is as follows:
X c ＝X initial (c+1)/2
wherein, X c To map the initial distribution population, X initial For initializing feasible dispatching distribution population with dimension D and randomly generating by cubic chaotic mapping, T is current iteration number, and T is current iteration number max Is the maximum iteration number;
the setting parameters comprise an upper limit up and a lower limit down of a set search space, an update density factor alpha, an adaptive inertia factor w, a current iteration time T and a maximum iteration time T.
The electric energy meter logistics scheduling method for the large-scale batch rotation task, provided by the invention, comprises the following steps of: calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating a novel distribution scheme,
in the updating process of the basic badger algorithm, the algorithm is iteratively updated through a mining stage and a honey collecting stage;
in the mining stage, the basic algorithm of the badger is used for global search, and the specific formula is as follows:
x new ＝x prey +F·β·I·x prey +F·r 1 ·α·d i ·cos(2πr 2 )[1-cos(2πr 3 )]
wherein x is new Is a new location of the individual badger prey The current global most position; beta is the ability of the individual with the badger to obtain food, r 1 、r 2 、r 3 And r 4 Is [0, 1]]Unequal random numbers, wherein F is a mark for changing the searching direction parameter of the badger, and I is the smell intensity of the prey;
if the smell intensity is higher, the searching speed of the badger is higher;
if the odor intensity is lower, the searching speed of the badger is lower;
in the honey collection stage, the basic algorithm for the badger carries out local search, and the specific formula is as follows:
x new ＝x prey +Fgrand·α·d i
wherein rand is [0, 1]]Random number in between, F, alpha, d i And respectively solving by using a formula in the mining optimization stage.
The electric energy meter logistics scheduling method for the large-scale batch rotation task, provided by the invention, comprises the following steps of: determining whether the iteration of the algorithm has stuck to stall includes,
if the algorithm falls into a stagnation stage, local range disturbance updating is carried out through an elite strategy;
firstly, ordering order population is sorted according to fitness value, the order population is divided into dominant population and disadvantaged population according to the current average fitness function value, the dominant order population is subjected to local range disturbance updating according to an elite strategy, the disadvantaged order population is updated according to a random disturbance mode, and the specific formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the position of the badger of the next generation,
the epsilon is a normal distribution obeying 0-1 for the current optimal individual positions of the badgers,
is the position of the individual in the current generation population,
is the position of any individual badger in the current population f average As a function of the current average fitness function, f i Is the current individual fitness function value.
If the order is not stagnated, selecting a parent order dispatching population and a newly generated dispatching order population selection dominant dispatching scheme according to a greedy strategy to form a novel distribution population;
wherein, when generating the new distribution population, checking whether an iteration condition is met.
The electric energy meter logistics scheduling method for the large-scale batch rotation task, provided by the invention, comprises the following steps of: checking whether iteration conditions are met when generating a new distribution population includes,
if t is less than or equal to t max Then repeatedly generating a novel distribution population;
if t > t max And outputting the optimal order scheduling scheme.
In a second aspect, an embodiment of the present invention provides a large-scale batch rotation task-oriented electric energy meter logistics scheduling system, including,
the model building module is used for building a logistics scheduling model of the electric energy meter for large-scale batch alternate tasks;
the calculation module is used for calculating the fitness value of each distribution scheme through a basic badger optimization algorithm, taking the minimum fitness value as the optimal fitness value, calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating the novel distribution scheme;
and the output module is used for judging whether the algorithm iteration is stuck to the stagnation.
In a third aspect, an embodiment of the present invention provides a computing device, including:
a memory and a processor;
the memory is used for storing computer-executable instructions, and the processor is used for executing the computer-executable instructions, and the computer-executable instructions when executed by the processor realize the steps of the electric energy meter logistics scheduling method facing the large-scale batch rotation task in any one of claims 1 to 7.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the method for scheduling electric energy meter logistics for large-scale batch rotation tasks as claimed in any one of claims 1 to 7 are implemented.
The invention has the beneficial effects that: the algorithm has high convergence speed, high precision and strong stability, can effectively improve the logistics dispatching and delivery efficiency of the electric energy meters for large-scale batch alternation tasks, and reduces the transportation cost; the model objective function provided by the invention is clear and easy to understand, the decision variables and the constraint conditions conform to the actual conditions of electric energy meter logistics transportation, the economic benefit and efficiency of the logistics transportation can be effectively improved, and the reasonable distribution of large-scale batch alternate logistics scheduling is realized.
Drawings
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 flowchart of a large-scale batch rotation task oriented electric energy meter logistics scheduling method and system.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
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, the references herein to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic that may be included in at least one implementation of the present 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.
Furthermore, the present invention is described in detail with reference to the drawings, and in the detailed description of the embodiments of the present invention, the cross-sectional view of the device structure is not enlarged partially according to the general scale for convenience of illustration, and the drawings are only exemplary and should not be taken 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.
Example 1
Referring to fig. 1, in order to provide a method and a system for logistics scheduling of an electric energy meter for a large-scale batch rotation task according to an embodiment of the present invention, the method includes:
as shown in fig. 1, the steps of the present invention are specifically as follows:
s1: and collecting total transportation time, distribution cost, penalty cost and vehicle idle rate data to establish an electric energy meter logistics scheduling model of a large-scale batch rotation task. It should be noted that:
the electric energy meter logistics scheduling model objective function of the large-scale batch rotation task is as follows:
therein, sigma A＝1 、∑ i＝1 、∑ j＝1 、∑ k＝1 Respectively the total transportation time, the distribution cost, the punishment cost and the vehicle empty load rate,
the total transportation cost of the A-th vehicle with the model number m in the transportation process,
Is the total time of transport of the vehicle Am,
for the total rest time of the vehicle, the total driving time of two drivers in 24 hours is 16 hours, i.e. 8 hours of rest every full 16 hours, C Am To penalize the cost, W Am Is the vehicle empty rate.
Total transport costs
The specific formula is as follows:
wherein Am is the model of the A-th vehicle, m is the vehicle model, i is the ith delivery point, j is the jth delivery point, u is the unit price per kilometer of the vehicle with the model m, wAm i Tonnage of goods, wAm, loaded from ith delivery point for type m A vehicle j Tonnage of goods transported to jth receiving point by an A-th vehicle with the model number m,
number of k types of material, L, loaded from the ith delivery point for the Am-th vehicle ij For the path from the ith delivery point to the jth receiving point, ω k For the kth notation, the basis weight, L jj ' is a path from the jth ship-to point to the jth ship-to point;
total time of transport
The specific formula is as follows:
wherein, tAm i Delivery time of the ith delivery Point, TAm, for the A-th vehicle of type m ij For the distance from the ith delivery point to the jth delivery point of the A-th vehicle of type m, Σ TAm jj′ For the total route time from the jth pick-up point to the jth' pick-up point for the type m of the a-th vehicle,
and the total warehousing time of the A-th vehicle with the model m at the jth receiving point.
Total rest time
The specific formula is as follows:
penalty cost C Am The concrete formula is as follows:
C Am ＝∑ A＝1 β Am ×ε；
therein, sigma A＝1 β Am And epsilon is the penalty cost of the empty load rate of each vehicle exceeding 5 percent, wherein epsilon is the total number of all vehicles of which the empty load rate of the vehicle Am exceeds 5 percent.
Vehicle empty load rate W Am The concrete formula is as follows:
wherein, wAm max At maximum vehicle load, wAm i Is at presentThe loading rate of the vehicle.
All constraints are also required to be satisfied in the calculation process of the objective function.
Constraints include vehicle delivery point origination constraints, vehicle loading limit constraints, maximum sunrise library constraints, permit time constraints, and quantity per model constraints.
Vehicle delivery point origination constraints:
the vehicle can only be loaded with originations from one delivery point.
Vehicle service people number constraint:
only 3 customers can be served for one vehicle at the most.
Vehicle loading limit constraints:
the weight limit loaded by the vehicle Am is determined by the vehicle's own performance.
Wherein, ω Am min 、ωAm max The maximum and minimum load weight of the vehicle Am.
Maximum daily ex-warehouse constraint:
the upper daily shipping limit for the delivery point i of the distribution center is determined by the warehouse structure, the staff and the working time.
Wherein the content of the first and second substances,
daily aggregate load for the ith delivery point, w i The maximum daily shipment volume for the ith delivery point.
And (3) carrying out time constraint of certificate of authority:
for transportation Time restrictions, determined by the point of delivery, where Time j From production delivery point i to delivery point for vehicle AmThe maximum time of the delivery point j.
Vehicle type quantity constraint:
for the ith delivery point model number of m,
total number of vehicles of type m for the ith delivery point.
S2: and constructing a mapping initial distribution population with high ergodicity through cubic chaotic mapping, and setting parameters. It should be noted that:
constructing a mapping initial distribution population with high ergodicity by cubic chaotic mapping, and setting parameters comprising
The formula of the cubic chaotic map is as follows:
c(o+1)＝4c(o) 3 -3y(o)-1<c(0)<1，c(o)≠0o＝0，1，2，...
wherein c is a chaotic variable, and the initial population of the badgers is set to be composed of Nop D-dimensional individuals, so that Nop feasible distribution populations can be initialized.
A random number rand between-1, 1 is generated as the position of the first dimension in each population individual.
Generating subsequent D-1 individuals of each dimension in the population individuals by an iterative method, and mapping variable values generated by cubic mapping into the individual of the population of the badger, wherein the specific formula is as follows:
X c ＝X initial (c+1)/2
wherein, X c To map the initial distribution population, X initial For initializing a feasible dispatching distribution population with dimension D and randomly generated by cubic chaotic mapping, T is the current iteration number, T max Is the maximum number of iterations.
The setting parameters comprise setting an upper limit up and a lower limit down of a search space, updating a density factor alpha, an adaptive inertia factor w, a current iteration time T and a maximum iteration time T.
S3: and calculating the fitness value of each distribution scheme by a basic badger optimization algorithm and taking the minimum fitness value as the optimal fitness value.
S4: and calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating a novel distribution scheme. It should be noted that:
the basic algorithm of the badger is used for carrying out iterative updating on the algorithm through a mining stage and a honey collecting stage in the updating process.
In the mining stage, the basic algorithm of the badger is used for global search, and the specific formula is as follows:
x new ＝x prey +F·β·I·x prey +F·r 1 ·α·d i ·cos(2πr 2 )[1-cos(2πr 3 )]
wherein x is new Is a new location of the individual badger prey Is the current global most significant position; beta is the food acquisition capacity of the individual meliger and the value is more than 1,r 1 、r 2 、r 3 And r 4 Is [0, 1]]Unequal random numbers exist, F is a mark for changing the searching direction parameter of the badger, and I is the smell intensity of the prey.
If the smell intensity is higher, the searching speed of the badger is higher.
The lower the odor intensity, the slower the search speed of the badger.
Alpha is a density factor, so that the smooth transition of the algorithm from global search to local search is ensured, and the mathematical model is as follows:
wherein F is a mark for changing the search direction, C is a constant, t is the current iteration number, and t is max To the total number of iterations, I i The odor intensity of each meliger in the population, S as the source intensity, d i The relative distance between the current global optimal position and the current individual meliger position,
source intensity S and relative distance d i The mathematical formula of (2) is as follows:
S＝(x i -x i+1 ) 2
d i ＝x prey -x i
in the honey collection stage, the basic algorithm for the badger carries out local search, and the specific formula is as follows:
x new ＝x prey +Fgrand·α·d i
wherein rand is [0, 1]]Random number in between, F, alpha, d i And respectively solving by using a formula in the mining optimization stage.
S5: and judging whether the iteration of the algorithm falls into stagnation or not. It should be noted that:
and if the algorithm falls into a stagnation stage, carrying out local range disturbance updating through an elite strategy.
Firstly, ordering order population is sorted according to fitness value, the order population is divided into a dominant population and a disadvantaged population according to a current average fitness function value, the dominant order population is subjected to local range disturbance updating according to an elite strategy, the disadvantaged order population is updated according to a random disturbance mode, and the specific formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the position of the badger of the next generation,
the epsilon is a normal distribution obeying 0-1 for the current optimal individual positions of the badgers,
is the position of the individual in the contemporary population,
is the position of any individual badger in the current population f average Is the current mean fitness function value, f i Is the current individual fitness function value.
And if the order is not in the stagnation stage, selecting a parent order dispatching population and a newly generated dispatching scheme with the dispatching order population selection advantage according to a greedy strategy to form a novel distribution population.
Wherein, when generating the novel distribution population, checking whether an iteration condition t is not more than t max ；
Checking whether iteration conditions are met when generating a novel distribution population includes if t is less than or equal to t max And repeatedly generating the novel distribution population.
If t > t max And outputting the optimal order scheduling scheme.
This embodiment also provides a large-scale batch turns electric energy meter logistics scheduling system of task, includes:
and the model building module is used for building a logistics scheduling model of the electric energy meter for large-scale batch rotation tasks.
And the calculation module is used for calculating the fitness value of each distribution scheme through a basic badger optimization algorithm, taking the minimum fitness value as the optimal fitness value, calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating the novel distribution scheme.
And the output module is used for judging whether the algorithm iteration is trapped in stagnation or not.
The embodiment also provides a computing device, which is suitable for a situation of an electric energy meter logistics scheduling method for large-scale batch rotation tasks, and includes:
a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, so as to implement the electric energy meter logistics scheduling method facing the large-scale batch rotation task, as provided by the above embodiments.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and an input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The communication interface of the computer device is used for communicating with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for implementing the logistics scheduling of the electric energy meter for large-scale batch rotation task as set forth in the above embodiments is implemented.
The storage medium proposed by the present embodiment belongs to the same inventive concept as the data storage method proposed by the above embodiments, and technical details that are not described in detail in the present embodiment can be referred to the above embodiments, and the present embodiment has the same beneficial effects as the above embodiments.
Example 2
The invention provides a verification test of the electric energy meter logistics scheduling method and system for large-scale batch rotation tasks for another embodiment, and the technical effect adopted in the method is verified and explained.
Comparing the method with a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA) and a differential evolution algorithm (DE), wherein test data are simulated Yunnan province electric energy meter transportation tasks, 10 bus lines and 3 vehicle types, 20 simulation experiments are carried out, and the results of the experiments are as follows:
table 1: and 4 algorithms are used for distributing each actual index value of the network average index value.
It can be seen that the present invention reduces the storage costs by 3.9%,8.2%,4.5% respectively, reduces the average working time by 15.4%,8.5%,12.7% respectively, and reduces the distribution path length by 14.3%,7.4%,11.7% respectively, compared to the other 3 methods.
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 is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to 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 (10)
1. The electric energy meter logistics scheduling method for the large-scale batch rotation task is characterized by comprising the following steps of:
collecting total transportation time, distribution cost, punishment cost and vehicle idle rate data to establish an electric energy meter logistics scheduling model of large-scale batch rotation tasks;
constructing a mapping initial distribution population with high ergodicity through cubic chaotic mapping, and setting parameters;
calculating the fitness value of each distribution scheme by a basic badger optimization algorithm, and taking the minimum fitness value as an optimal fitness value;
calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating a novel distribution scheme;
and judging whether the iteration of the algorithm falls into stagnation or not.
2. The large-scale batch rotation task-oriented electric energy meter logistics scheduling method of claim 1, wherein: the electric energy meter logistics scheduling model for the large-scale batch rotation task comprises,
the objective function is:
therein, sigma A＝1 、∑ i＝1 、∑ j＝1 、∑ k＝1 Respectively the total transportation time, the distribution cost, the punishment cost and the vehicle empty load rate,
the total transportation cost of the A-th vehicle with the model number m in the transportation process,
Is the total time of transport of the vehicle Am,
as total rest time of the vehicle, C Am To penalize the cost, W Am Is the vehicle empty load rate;
all constraints need to be satisfied in the calculation process of the objective function.
3. The large-scale batch rotation task-oriented electric energy meter logistics scheduling method of claim 2, wherein: the constraints include a vehicle origin delivery constraint, a vehicle loading limit constraint, a maximum sunrise library constraint, a permit time constraint, and a quantity per vehicle constraint.
4. The large-scale batch rotation task-oriented electric energy meter logistics scheduling method of claim 1, wherein: constructing a mapping initial distribution population with high ergodicity by cubic chaotic mapping, and setting parameters comprising
The formula of the cubic chaotic map is as follows:
c(o+1)＝4c(o) 3 -3y(o)
-1<c(0)<1，c(o)≠0o＝0，1，2，...
wherein c is a chaotic variable, and the initial badger population is set to be composed of Nop D-dimensional individuals, namely Nop feasible distribution populations can be initialized;
generating a random number rand between [ -1,1] as a position of a first dimension in each population individual;
generating subsequent D-1 individuals of each dimension in the population individuals by an iterative method, and mapping variable values generated by cubic mapping into the individual of the population of the badger, wherein the specific formula is as follows:
X c ＝X initial (c+1)/2
wherein, X c To map the initial distribution population, X initial For initializing feasible dispatching distribution population with dimension D and randomly generating by cubic chaotic mapping, T is current iteration number, and T is current iteration number max Is the maximum number of iterations;
the setting parameters comprise setting an upper limit up and a lower limit down of a search space, updating a density factor alpha, an adaptive inertia factor w, a current iteration time T and a maximum iteration time T.
5. The large-scale batch rotation task-oriented electric energy meter logistics scheduling method of claim 1, wherein: calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating a novel distribution scheme,
in the updating process of the basic badger algorithm, the algorithm is iteratively updated through a mining stage and a honey collecting stage;
in the mining stage, the basic algorithm of the badger is used for global search, and the specific formula is as follows:
x new ＝x prey +F·β·I·x prey +F·r 1 ·α·d i ·cos(2πr 2 )[1-cos(2πr 3 )]
wherein x is new Is a new location of the individual badger prey Is the current global most significant position; beta is the ability of the individual with badger to obtain food, r 1 、r 2 、r 3 And r 4 Is [0,1 ]]Unequal random numbers are obtained, F is a mark for changing the searching direction parameter of the badger, and I is the smell intensity of the prey;
if the smell intensity is higher, the searching speed of the badger is higher;
if the odor intensity is lower, the searching speed of the badger is lower;
in the honey collection stage, the basic badger algorithm carries out local search, and the specific formula is as follows:
x new ＝x prey +Fgrand·α·d i
wherein rand is [0, 1]]Random number in between, F, alpha, d i And respectively solving by using formulas in the mining optimization stage.
6. The electric energy meter logistics scheduling method for the large-scale batch rotation task as recited in claim 1, wherein: determining whether the algorithm iteration falls into stalls includes,
if the algorithm falls into a stagnation stage, local range disturbance updating is carried out through an elite strategy;
firstly, ordering order population is sorted according to fitness value, the order population is divided into a dominant population and a disadvantaged population according to a current average fitness function value, the dominant order population is subjected to local range disturbance updating according to an elite strategy, the disadvantaged order population is updated according to a random disturbance mode, and the specific formula is as follows:
wherein the content of the first and second substances,
is the position of the badger of the next generation,
the epsilon is a normal distribution obeying 0-1 for the current optimal individual position of the badger,
is the position of the individual in the current generation population,
is the position of any individual badger in the current population f average Is the current mean fitness function value, f i Is the current individual fitness function value.
If the order is not stagnated, selecting a parent order dispatching population and a newly generated dispatching order population selection dominant dispatching scheme according to a greedy strategy to form a novel distribution population;
wherein, when generating the novel distribution population, checking whether an iteration condition is met.
7. The large-scale batch rotation task-oriented electric energy meter logistics scheduling method of claim 6, wherein: checking whether iteration conditions are met when generating a new distribution population includes,
if t is less than or equal to t max Then, generating a novel distribution population repeatedly;
if t > t max And outputting the optimal order scheduling scheme.
8. The electric energy meter logistics scheduling system for large-scale batch rotation tasks comprises,
the model building module is used for building a logistics scheduling model of the electric energy meter for large-scale batch alternate tasks;
the calculation module is used for calculating the fitness value of each distribution scheme through a basic badger optimization algorithm, taking the minimum fitness value as the optimal fitness value, calculating the odor intensity of each individual in the population according to the optimal fitness value to obtain a random number, and updating the novel distribution scheme;
and the output module is used for judging whether the algorithm iteration is stuck to the stagnation.
9. A computing device, comprising:
a memory and a processor;
the memory is used for storing computer-executable instructions, and the processor is used for executing the computer-executable instructions, and the computer-executable instructions when executed by the processor realize the steps of the electric energy meter logistics scheduling method facing the large-scale batch rotation task in any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the large scale batch rotation task oriented electric energy meter logistics scheduling method of any one of claims 1 to 7.
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| CN116916475A (en) * | 2023-08-10 | 2023-10-20 | 华东交通大学 | Wireless sensor network deployment method based on multi-strategy improved badger algorithm |
| CN117251280A (en) * | 2023-08-18 | 2023-12-19 | 湖北工业大学 | Cloud resource load balancing scheduling method, device, equipment and medium |
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| CN116916475A (en) * | 2023-08-10 | 2023-10-20 | 华东交通大学 | Wireless sensor network deployment method based on multi-strategy improved badger algorithm |
| CN116916475B (en) * | 2023-08-10 | 2024-05-07 | 华东交通大学 | Wireless sensor network deployment method based on multi-strategy improved badger algorithm |
| CN117251280A (en) * | 2023-08-18 | 2023-12-19 | 湖北工业大学 | Cloud resource load balancing scheduling method, device, equipment and medium |
| CN117251280B (en) * | 2023-08-18 | 2024-04-05 | 湖北工业大学 | Cloud resource load balancing scheduling method, device, equipment and medium |
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