CN109754354B - Method and apparatus for optimizing carbon information disclosure schemes - Google Patents
Method and apparatus for optimizing carbon information disclosure schemes Download PDFInfo
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Abstract
The invention relates to a method and a device for optimizing a carbon information disclosure scheme, wherein the method can solve the problem of the cooperation of enterprise carbon information disclosure, enterprise value increase and enterprise risk cost control under the information sharing behavior of investors based on an improved tree algorithm, can assist enterprises in making decisions about the carbon information disclosure degree to different investors, and can establish a perfect carbon information disclosure mechanism. In addition, compared with the traditional tree algorithm, the improved tree algorithm mainly improves the phenomena of premature convergence and local precocity in the traditional algorithm by introducing random individuals. In addition, the probability of selecting random positions by the algorithm is continuously adjusted in a self-adaptive manner in the iteration process by controlling the algorithm to search the trend factors, so that the algorithm is helped to jump out of local optimality in the later iteration stage, the diversity of the population is increased, and the global searching capability of the algorithm is improved.
Description
Technical Field
The embodiment of the invention relates to the field of carbon information processing, in particular to a method and a device for optimizing a carbon information disclosure scheme.
Background
With the continuous improvement of public environmental awareness, investors can consider not only the operation level and the profitability of enterprises but also the social environment responsibility born by the enterprises when selecting the investment enterprises. In order to attract more investments, enterprises can disclose own environmental pollution data to investors through a media platform, wherein carbon information is used as an important component of the environmental pollution data, has a mature information disclosure channel, and is most significant for the investors.
After obtaining the carbon information of the enterprise, each investor can disclose the carbon information to other investors to different degrees, and on the premise of considering the carbon information sharing behavior among the investors, relevant scholars study the carbon information disclosure mechanism and draw the following conclusion: the value of the enterprise will increase as the quality of the carbon information disclosure increases, and the financing constraints of the enterprise will decrease as the level of carbon information disclosure increases.
Currently, some scholars analyze the influence of carbon information disclosure level on enterprise financing constraints by using enterprise data of carbon information disclosure project (CDP), and other scholars construct a relation model between carbon information disclosure of listed companies and external financing constraints, and all draw the same conclusion: financing constraints for a business may decrease as the level of carbon information disclosure increases.
Other scholars further studied the relationship among carbon information disclosure, market environment and commercial credit financing using enterprise data based on carbon information disclosure project (CDP), and examined the impact of market environment on the relationship between carbon information disclosure and commercial credit financing. Still other scholars, from government regulatory perspective, have studied the relationship between carbon information disclosure and financing constraints and have found that carbon information disclosure has some mitigating effect on the financing constraints of the enterprise.
Other scholars study the influence of carbon information disclosure on enterprise financing scale based on panel data of chemical industry, and find that the carbon information disclosure quality of listed companies in the chemical industry is not related to the equity financing scale, but the carbon information disclosure quality of listed companies is positively related to the debt financing scale, and the higher the carbon information disclosure quality is, the larger the debt financing scale is.
However, in the process of the invention, the inventor finds that the prior art has the following defects:
the current related technologies mainly stay in the aspect of empirical analysis of the influence mechanism of carbon information disclosure on enterprise value and external financing, and cannot go deep into the aspect of a collaborative optimization method of carbon information disclosure, enterprise value and risk control.
In addition, in the existing tree species algorithm, because the essence of the algorithm is that the child tree species continuously migrate towards the existing individuals in the population in each iteration process, the child tree species are likely to move towards a local optimal solution in the later stage of the algorithm iteration, and fall into the early maturing predicament.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for optimizing a carbon information disclosure scheme, so as to solve at least one technical problem.
In a first aspect, an embodiment of the present invention provides a method for optimizing a carbon information disclosure scheme based on an improved tree algorithm, including:
step 1: initializing algorithm parameters, including: the method comprises the following steps of (1) counting the population POP, searching trend control parameters ST1 and ST2, problem dimension D and algorithm iteration termination time T, wherein D is N + 1;
step 2: initializing the initial position of the POP tree in the D-dimensional solution space, and recording the initial position as POP (0) { T } T1(0)…Tl(0)…TN(0) Wherein, T isl(0) Denotes the first tree in the initial population, Tl(0)＝{Tl1(0)…Tld(0)…TlD(0) Therein of，Tl1(0) Coefficient, T, representing the degree of basic carbon information revealed by the business to each investorld(0) Degree coefficient, T, representing proprietary carbon information revealed by the business to the d-1 st investorld(0) Is taken from 0 to 1-Tl1(0) Wherein D is more than or equal to 2 and less than or equal to D;
and step 3: calculating the fitness value of each tree in turn, and recording the fitness value as Fit (0) ═ F1(0)…Fl(0)…FN(0) In which Fl(0) Representing the fitness value of the first tree in the initial population, recording the individual with the largest fitness value of the current population as the current best solution B, and setting the current iteration time it to be 1;
and 4, step 4: the tree serial number l considered before is 1;
and 5: randomly setting the number SN of seeds of the first tree, belonging to [ 10% N, 25% N ];
step 6: let the currently considered seed sequence number sn ═ 1;
and 7: let d be 1;
and 8: the tree generates offspring tree species according to a rule comprising: migrating to random parent individuals in the population, migrating to optimal parent individuals in the population, and migrating to the direction of random positions;
and step 9: judging whether D is not greater than D, if so, changing D to D +1 and going to step 8;
step 10: sequentially judging whether the values from the 2 nd dimension to the D th dimension of the sn-th seed are less than or equal to 1-Tl1(it), if not, decreasing Tl1(it) such that the 2 nd to D nd dimension values of the sn-th seed are less than or equal to 1-Tl1(it) and calculating the fitness value of the sn-th seed;
step 11: judging whether SN is not greater than SN, if so, making SN ═ SN +1 and going to step 7;
step 12: judging whether the fitness value of the optimal seed of the first tree is smaller than that of the first tree or not, and if so, replacing the position of the first tree with the position of the seed;
step 13: judging whether l is not more than N, if so, changing l to l +1 and going to the step 5;
step 14: updating the current best solution B;
step 15: judging whether the current algorithm running time is not greater than T, if so, turning it to it +1 and turning to the step 4;
step 16: and outputting the current optimal solution B as an optimal carbon information disclosure scheme.
Optionally, step 8 comprises:
if rand < ST1, let Slsnd(it)＝Tld(it-1)+α(Bd-Tld(it-1))，Slsnd(it) represents the d-dimensional position of the sn-th seed of the first tree in the it-th generation population;
if it is
Then order Slsnd(it)＝Tld(it-1)+α(Trd(it-1)-Tld(it-1))；
If it is
Then order Slsnd(it) ═ Rand (), where Rand is random number, α is scaling factor, Rand () is random position, all the values are between 0 and 1, t is current running time of algorithm, B isdD-dimensional position, T, representing an optimal solutionrdRepresenting the d-dimension position of any individual in the population except for tree l.
Optionally, the method further comprises:
after the information is disclosed, determining the final price value change V of the enterprise according to the following formula:
Optionally, the method further comprises:
determining investor T according to the following formulaiCapital W of investment enterprisei：
Wherein invent () represents a function of the change of the investment amount with the degree of disclosure of the carbon information,
indicating the degree of disclosure of the proprietary carbon information provided by the business to the various investors,
indicating the degree of disclosure of shared carbon information, W, provided by the business to the various investorsiIn the interval [ Li，Ui]Fluctuating within the range.
The method further comprises the following steps: is determined according to the following formula
Optionally, the method further comprises:
v is determined according to the following formulaΔ：
Wherein value-add () is a value-added function of the enterprise value as financing capital increases,
is the total investment capital.
Optionally, the method further comprises:
according to the following formulaDetermining
Wherein value-loss () represents a risk loss function of enterprise value with carbon information disclosure.
Optionally, the method further comprises:
calculating the fitness of the first tree in the ith generation population according to the following stepsl(it)：
Step a: let d be 1, the first tree in the it generation population be denoted as Tl(it)＝{Tl1(it)…Tld(it)…TlD) it) }, order Wd＝invest(Tl1(it)+Tl(d+1)(it)+share(∑j∈{2，…，D，j≠d+1Tlj(it))), etc
Wherein share () represents a function of shared carbon information between the respective investors;
step b: if D is less than or equal to D-1, making D equal to D +1, otherwise, proceeding to the next step;
step c: output of
Optionally, the invent () is an increasing function.
In a second aspect, an embodiment of the present invention provides an apparatus for optimizing a carbon information disclosure scheme, including:
a first initialization module, configured to perform step 1: initializing algorithm parameters, including: the method comprises the following steps of (1) counting the population POP, searching trend control parameters ST1 and ST2, problem dimension D and algorithm iteration termination time T, wherein D is N + 1;
second initializationA module for performing step 2: initializing the initial position of the POP tree in the D-dimensional solution space, and recording the initial position as POP (0) { T } T1(0)…Tl(0)…TN(0) Wherein, T isl(0) Denotes the first tree in the initial population, Tl(0)＝{Tl1(0)…Tld(0)…TlD(0) In which T isl1(0) Coefficient, T, representing the degree of basic carbon information revealed by the business to each investorld(0) Degree coefficient, T, representing proprietary carbon information revealed by the business to the d-1 st investorld(0) Is taken from 0 to 1-Tl1(0) Wherein D is more than or equal to 2 and less than or equal to D;
a fitness calculation module for executing step 3: calculating the fitness value of each tree in turn, and recording the fitness value as Fit (0) ═ F1(0)…Fl(0)…FN(0) In which Fl(0) Representing the fitness value of the first tree in the initial population, recording the individual with the largest fitness value of the current population as the current best solution B, and setting the current iteration time it to be 1;
a first setting module, configured to perform step 4: the tree serial number l considered before is 1;
a second setting module, configured to perform step 5: randomly setting the number SN of seeds of the first tree, belonging to [ 10% N, 25% N ];
a third setting module, configured to perform step 6: let the currently considered seed sequence number sn ═ 1;
a fourth setting module, configured to perform step 7: let d be 1;
a generating module for executing step 8: the tree generates offspring tree species according to a rule comprising: migrating to random parent individuals in the population, migrating to optimal parent individuals in the population, and migrating to the direction of random positions;
a first determining module, configured to perform step 9: judging whether D is not greater than D, if so, changing D to D +1 and going to step 8;
a second determining module, configured to perform step 10: sequentially judging whether the values from the 2 nd dimension to the D th dimension of the sn-th seed are less than or equal to 1-Tl1(it), if not, decreasing Tl1(it) makes the sn-th seedIs less than or equal to 1-Tl1(it) and calculating the fitness value of the sn-th seed;
a third determining module, configured to perform step 11: judging whether SN is not greater than SN, if so, making SN ═ SN +1 and going to step 7;
a fourth determining module, configured to perform step 12: judging whether the fitness value of the optimal seed of the first tree is smaller than that of the first tree or not, and if so, replacing the position of the first tree with the position of the seed;
a fifth judging module, configured to execute step 13: judging whether l is not more than N, if so, changing l to l +1 and going to the step 5;
an update module for performing step 14: updating the current best solution B;
a sixth determining module, configured to perform step 15: judging whether the current algorithm running time is not greater than T, if so, turning it to it +1 and turning to the step 4;
an output module for performing step 16: and outputting the current optimal solution B as an optimal carbon information disclosure scheme.
The invention has the following beneficial effects:
1. the improved tree algorithm disclosed by the invention can solve the problem of the cooperation of enterprise carbon information disclosure, enterprise value increase and enterprise risk cost control under the information sharing behavior of investors, can assist the enterprise in making decisions on the carbon information disclosure degree of different investors, and establishes a perfect carbon information disclosure mechanism.
2. Compared with the traditional tree algorithm, the improved tree algorithm mainly improves the phenomena of premature convergence and local prematurity in the traditional algorithm by introducing random individuals.
3. By controlling the algorithm to search the trend factors, the probability of selecting random positions by the algorithm is continuously adjusted in a self-adaptive manner in the iteration process, so that the algorithm is helped to jump out of local optimum in the later iteration stage, the diversity of the population is increased, and the global search capability of the algorithm is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic diagram of an enterprise carbon information three-tier disclosure and enterprise financing framework in accordance with an embodiment of the present invention;
fig. 2 is a flow chart of a method for optimizing a carbon information disclosure scheme according to an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
In order to solve the problem of enterprise carbon information disclosure, value increment and risk control collaborative optimization based on an improved tree algorithm under an investor information sharing behavior, the embodiment of the invention provides a method for optimizing a carbon information disclosure scheme. The present invention is directed to determining the degree of carbon emissions information that enterprise a discloses to each investor's decision to maximize the value increase of the enterprise. The present invention employs a modified tree algorithm to solve the extent to which each business reveals carbon information to each investor. In order to overcome the limitation of premature convergence of the traditional tree seed algorithm, the method introduces partial random individuals in the iteration process to increase the diversity of the population, dynamically self-adapts random factors, increases the probability of selecting the random individuals in the later period of the algorithm, and helps the algorithm to jump out of local optimum in the later period to a certain extent.
Within a certain information disclosure range, the investment amount of investors can be increased along with the increase of the carbon emission information disclosure degree, and the value of enterprises is increased accordingly. However, considering that a certain business risk and opportunity cost loss are faced in the information disclosure process, the value loss of the enterprise increases with the increase of the information disclosure degree of the enterprise. The problem parameters and associated models involved are set forth below from an investor and business perspective, respectively. On the part of investors, the investors can judge the operation health condition and investment prospect of the enterprise according to the carbon emission environment information (including basic carbon information and exclusive carbon information) disclosed by the enterprise A and the carbon emission information obtained from other investors, and then decide whether to invest and invest by money.
Referring to fig. 1, fig. 1 is a schematic diagram of an enterprise carbon information three-tier disclosure and enterprise financing framework in an embodiment of the present invention. As shown in fig. 1, in a market environment considering carbon emission limitation, assuming that there are N potential investors of enterprise a on the market, these investors rely on financial information and social responsibility information disclosed by the enterprise to evaluate the investment prospects of enterprise a due to the phenomenon of information asymmetry. The disclosure of carbon emission information of enterprise a can be described in three levels, i.e., basic carbon information, exclusive carbon information, and shared carbon information, and fig. 1 visually expresses the process of investor investment and enterprise public carbon emission information.
The definitions of the terms referred to in FIG. 1 are as follows:
basic carbon information: the basic carbon information refers to basic information of carbon emission actively disclosed to the public by the enterprise A, and part of information data can be disclosed and shared to all investors. In other words, the basic carbon emission information obtained by the respective investors is consistent and does not differ in degree.
Proprietary carbon information: the exclusive carbon information refers to carbon emission information provided by the enterprise a to different levels for stimulating investment intentions of different investors, so that the exclusive carbon emission information obtained by the investors is different in disclosure level.
Sharing carbon information: after acquiring the carbon emission information of the enterprise A, each investor can disclose the carbon emission information to other investors to different degrees, so that each investor can arrange and generate a part of additional carbon emission information on the basis of the acquired carbon emission information according to the carbon emission information disclosed by other investors, and the part of carbon emission data information is called shared carbon information.
Referring to fig. 2, fig. 2 is a flowchart of a method for optimizing a carbon information disclosure scheme according to an embodiment of the present invention. As shown in fig. 2, a method for optimizing a carbon information disclosure scheme according to an embodiment of the present invention includes the following steps:
the method comprises the following steps: and setting initial parameters. Specifically, the first step includes the following substeps:
initializing algorithm parameters, including: the method comprises the following steps of population number POP, search trend control parameters ST1 and ST2, problem dimension D and algorithm iteration termination time T, wherein D is N + 1.
Step two: the iteration begins and the tree position is initialized. Specifically, the second step includes the following substeps:
initializing the initial position of the POP tree in the D-dimensional solution space, and recording the initial position as POP (0) { T } T1(0)…Tl(0)…TN(0) Wherein, T isl(0) Denotes the first tree in the initial population, Tl(0)＝{Tl1(0)…Tld(0)…TlD(0) In which T isl1(0) Coefficient, T, representing the degree of basic carbon information revealed by the business to each investorld(0) Degree coefficient, T, representing proprietary carbon information revealed by the business to the d-1 st investorld(0) Is taken from 0 to 1-Tl1(0) Wherein D is more than or equal to 2 and less than or equal to D.
Step three: the tree generates offspring tree species according to a rule comprising: migrating to random parent individuals in the population, migrating to optimal parent individuals in the population, and migrating to random positions. Specifically, step three includes the following substeps:
if rand < ST1, let Slsnd(it)＝Tld(it-1)+α(Bd-Tld(it-1))，Slsnd(it) represents the d-dimensional position of the sn-th seed of the first tree in the it-th generation population;
if it is
Then order Slsnd(it)＝Tld(it-1)+α(Trd(it-1)-Tld(it-1))；
If it is
Then order Slsnd(it) ═ Rand (), where Rand is random number, α is scaling factor, Rand () is random position, all the values are between 0 and 1, t is current running time of algorithm, B isdD-dimensional position, T, representing an optimal solutionrdRepresenting the d-dimension position of any individual in the population except for tree l.
Random individuals are added to the improved tree algorithm, so that the algorithm can jump out of local optima in the iterative process, and the global searching capability of the algorithm is expanded. By controlling the algorithm to search the trend factors, the probability of selecting the random position by the algorithm is continuously adjusted in a self-adaptive manner in the iterative process, and finally the probability of selecting the random position by the algorithm in the later operation stage is continuously increased, which is mainly embodied in the third step.
Step four: calculating the fitness of the filial generation and updating the population.
Step five: judging whether all the population is traversed, if so, entering a sixth step; if not, returning to the third step.
Step six: and updating the iteration times.
Step seven: judging whether the algorithm termination time is reached, if so, entering the step eight; if not, returning to the step two.
Step eight: and outputting the optimal carbon information disclosure scheme.
Specifically, the method for optimizing the carbon information disclosure scheme provided by the embodiment of the invention comprises the following steps:
step 1: initializing algorithm parameters, including: the method comprises the following steps of (1) counting the population POP, searching trend control parameters ST1 and ST2, problem dimension D and algorithm iteration termination time T, wherein D is N + 1;
step 2: initializing the initial position of the POP tree in the D-dimensional solution space, and recording the initial position as POP (0) { T } T1(0)…Tl(0)…TN(0) Wherein, T isl(0) Denotes the first tree in the initial population, Tl(0)＝{Tl1(0)…Tld(0)…TlD(0) In which T isl1(0) Representing individual contributions by a businessBasic carbon information degree coefficient, T, disclosed by the inventorld(0) Degree coefficient, T, representing proprietary carbon information revealed by the business to the d-1 st investorld(0) Is taken from 0 to 1-Tl1(0) Wherein D is more than or equal to 2 and less than or equal to D;
and step 3: calculating the fitness value of each tree in turn, and recording the fitness value as Fit (0) ═ F1(0)…Fl(0)…FN(0) In which Fl(0) Representing the fitness value of the first tree in the initial population, recording the individual with the largest fitness value of the current population as the current best solution B, and setting the current iteration time it to be 1;
and 4, step 4: the tree serial number l considered before is 1;
and 5: randomly setting the number SN of seeds of the first tree, belonging to [ 10% N, 25% N ];
step 6: let the currently considered seed sequence number sn ═ 1;
and 7: let d be 1;
and 8: the tree generates offspring tree species according to a rule comprising: migrating to random parent individuals in the population, migrating to optimal parent individuals in the population, and migrating to the direction of random positions;
and step 9: judging whether D is not greater than D, if so, changing D to D +1 and going to step 8;
step 10: sequentially judging whether the values from the 2 nd dimension to the D th dimension of the sn-th seed are less than or equal to 1-Tl1(it), if not, decreasing Tl1(it) such that the 2 nd to D nd dimension values of the sn-th seed are less than or equal to 1-Tl1(it) and calculating the fitness value of the sn-th seed;
step 11: judging whether SN is not greater than SN, if so, making SN ═ SN +1 and going to step 7;
step 12: judging whether the fitness value of the optimal seed of the first tree is smaller than that of the first tree or not, and if so, replacing the position of the first tree with the position of the seed;
step 13: judging whether l is not more than N, if so, changing l to l +1 and going to the step 5;
step 14: updating the current best solution B;
step 15: judging whether the current algorithm running time is not greater than T, if so, turning it to it +1 and turning to the step 4;
step 16: and outputting the current optimal solution B as an optimal carbon information disclosure scheme.
In one embodiment, the method further comprises:
determining investor T according to the following formulaiCapital W of investment enterprisei：
Wherein invent () represents a function of the change of the investment amount with the degree of disclosure of the carbon information,
indicating the degree of disclosure of the proprietary carbon information provided by the business to the various investors,indicating the degree of disclosure of shared carbon information, W, provided by the business to the various investorsiIn the interval [ Li，Ui]Fluctuating within the range.
In the investment decision process, the investment willingness of each investor to the enterprise A is different, and in consideration of the influence of carbon emission information disclosure on the investment willingness of the investor, an investor T is assumediWilling to invest a capital W of an Enterprise AiIn the interval [ Li，Ui]Fluctuating within the range. Investors will reveal degree C according to the basic carbon information of enterprise AbasicThe degree of disclosure of the exclusive carbon information given to it by the enterprise AAnd degree of disclosure of shared carbon information
The specific investment amount can be expressed as the following functional relationship:
in one embodiment, the invent () is an increasing function.
The carbon information disclosure degree and the financing amount have a positive correlation, and therefore invent () is an increasing function.
In one embodiment, the method further comprises:
v is determined according to the following formulaΔ：
Wherein value-add () is a value-added function of the enterprise value as financing capital increases,is the total investment capital.
In the case of business a, on the one hand, due to the disclosure of carbon emission information, investors' funds will increase, which in turn will lead to an increase in business value. Suppose enterprise value increases VΔAnd total investment capital
In a relationship of
In one embodiment, the method further comprises:
is determined according to the following formula
Wherein value-loss () represents a risk loss function of enterprise value with carbon information disclosure.
In the case of Enterprise A, anotherOn the other hand, excessive carbon emission information disclosure may pose certain business risks, especially in view of information sharing activities among investors. Suppose a loss of value for an enterprise
The relationship with the degree of disclosure of carbon emission information is
Wherein value-loss () represents a risk loss function of enterprise value with carbon information disclosure.
In one embodiment, the method further comprises:
is determined according to the following formula
In one embodiment, the method further comprises:
after the information is disclosed, determining the final price value change V of the enterprise according to the following formula:
Enterprise a needs to decide the appropriate degree of carbon emission information disclosure to achieve the greatest increase in enterprise value. After the information is disclosed, the final price value change of the enterprise can be expressed as
Can also be expressed as
In one embodiment, the method further comprises:
determining investor T according to the following formulaiCapital W of investment enterprisei：
Wherein invent () represents a function of the change of the investment amount with the degree of disclosure of the carbon information,
indicating the degree of disclosure of the proprietary carbon information provided by the business to the various investors,
indicating the degree of disclosure of shared carbon information, W, provided by the business to the various investorsiIn the interval [ Li，Ui]Fluctuating within the range.
In the investment decision process, the investment willingness of each investor to the enterprise A is different, and in consideration of the influence of carbon emission information disclosure on the investment willingness of the investor, an investor T is assumediWilling to invest a capital W of an Enterprise AiIn the interval [ Li，Ui]Fluctuating within the range. Investors will reveal degree C according to the basic carbon information of enterprise AbasicThe degree of disclosure of the exclusive carbon information given to it by the enterprise A
And degree of disclosure of shared carbon information
The specific investment amount can be expressed as the following functional relationship:
wherein invent () represents a function of the amount of investment as a function of the degree of disclosure of the carbon information.
In one embodiment, the method further comprises:
calculating the fitness of the first tree in the ith generation population according to the following stepsl(it)：
Step a: let d be 1, the first tree in the it generation population be denoted as Tl(it)＝{Tl1(it)…Tld(it)…TlD(it) }, let Wd＝invest(Tl1(it)+Tl(d+1)(it)+share(∑j∈{2，…，D，j≠d+1Tlj(it))), etc
Wherein share () represents a function of shared carbon information between the respective investors;
step b: if D is less than or equal to D-1, making D equal to D +1, otherwise, proceeding to the next step;
step c: output of
The enterprise controls the financing level and enterprise risk of the enterprise by deciding the degree of carbon information that is revealed to the individual investors. The improved tree algorithm can determine the degree of carbon information disclosed by the enterprise to each investor with the aim of maximum algorithm enterprise value increase. For the first tree in the it generation population, the fitness of the first tree is fitnessl(it) is calculated as follows:
(1) let d be 1, the first tree in the it generation population be denoted as Tl(it)＝{Tl1(it)…Tld(it)…TlD(it)}
Let Wd＝invest(Tl1(it)+Tl(d+1)(it)+share(∑j∈{2，…，D，j≠d+1Tlj(it))) and
(2) and if D is less than or equal to D-1, making D equal to D +1, otherwise, carrying out the next step.
(3) Output of
Based on the same inventive concept, the embodiment of the invention provides a device for optimizing a carbon information disclosure scheme. The device includes:
a first initialization module, configured to perform step 1: initializing algorithm parameters, including: the method comprises the following steps of (1) counting the population POP, searching trend control parameters ST1 and ST2, problem dimension D and algorithm iteration termination time T, wherein D is N + 1;
a second initialization module, configured to perform step 2: initializing the initial position of the POP tree in the D-dimensional solution space, and recording the initial position as POP (0) { T } T1(0)…Tl(0)…TN(0) Wherein, T isl(0) Denotes the first tree in the initial population, Tl(0)＝{Tl1(0)…Tld(0)…TlD(0) In which T isl1(0) Coefficient, T, representing the degree of basic carbon information revealed by the business to each investorld(0) Degree coefficient, T, representing proprietary carbon information revealed by the business to the d-1 st investorld(0) Is taken from 0 to 1-Tl1(0) Wherein D is more than or equal to 2 and less than or equal to D;
a fitness calculation module for executing step 3: calculating the fitness value of each tree in turn, and recording the fitness value as Fit (0) ═ F1(0)…Fl(0)…FN(0) In which Fl(0) Expressing the fitness value of the first tree in the initial population, and recording the individual with the maximum fitness value of the current population as the current best solutionB, setting the current iteration time it to be 1;
a first setting module, configured to perform step 4: the tree serial number l considered before is 1;
a second setting module, configured to perform step 5: randomly setting the number SN of seeds of the first tree, belonging to [ 10% N, 25% N ];
a third setting module, configured to perform step 6: let the currently considered seed sequence number sn ═ 1;
a fourth setting module, configured to perform step 7: let d be 1;
a generating module for executing step 8: the tree generates offspring tree species according to a rule comprising: migrating to random parent individuals in the population, migrating to optimal parent individuals in the population, and migrating to the direction of random positions;
a first determining module, configured to perform step 9: judging whether D is not greater than D, if so, changing D to D +1 and going to step 8;
a second determining module, configured to perform step 10: sequentially judging whether the values from the 2 nd dimension to the D th dimension of the sn-th seed are less than or equal to 1-Tl1(it), if not, decreasing Tl1(it) such that the 2 nd to D nd dimension values of the sn-th seed are less than or equal to 1-Tl1(it) and calculating the fitness value of the sn-th seed;
a third determining module, configured to perform step 11: judging whether SN is not greater than SN, if so, making SN ═ SN +1 and going to step 7;
a fourth determining module, configured to perform step 12: judging whether the fitness value of the optimal seed of the first tree is smaller than that of the first tree or not, and if so, replacing the position of the first tree with the position of the seed;
a fifth judging module, configured to execute step 13: judging whether l is not more than N, if so, changing l to l +1 and going to the step 5;
an update module for performing step 14: updating the current best solution B;
a sixth determining module, configured to perform step 15: judging whether the current algorithm running time is not greater than T, if so, turning it to it +1 and turning to the step 4;
an output module for performing step 16: and outputting the current optimal solution B as an optimal carbon information disclosure scheme.
Since the apparatus for optimizing a carbon information disclosure scheme described in this embodiment is an apparatus that can perform the method for optimizing a carbon information disclosure scheme in the embodiment of the present invention, based on the method for optimizing a carbon information disclosure scheme described in the embodiment of the present invention, a person skilled in the art can understand a specific implementation manner of the apparatus for optimizing a carbon information disclosure scheme of this embodiment and various modifications thereof, so that a detailed description of how the apparatus for optimizing a carbon information disclosure scheme achieves the method for optimizing a carbon information disclosure scheme in the embodiment of the present invention is not provided here. The scope of the present application is intended to encompass any apparatus that can be used by those skilled in the art to practice the method of the present invention for optimizing the carbon information disclosure.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this disclosure.
Claims (3)
1. A method for optimizing carbon information disclosure schemes based on modified tree algorithm, wherein the method is used for determining the degree of carbon information disclosed by a business to each investor, the method comprises:
step 1: initializing algorithm parameters, including: the method comprises the following steps of (1) counting the population POP, searching trend control parameters ST1 and ST2, problem dimension D and algorithm iteration termination time T, wherein D is N + 1;
step 2: initializing the initial position of the POP tree in the D-dimensional solution space, and recording the initial position as POP (0) { T } T1(0)…Tl(0)…TN(0) In which T isl(0) Denotes the first tree in the initial population, Tl(0)＝{Tl1(0)…Tld(0)…TlD(0) In which T isl1(0) Coefficient, T, representing the degree of basic carbon information revealed by the business to each investorld(0) Degree coefficient, T, representing proprietary carbon information revealed by the business to the d-1 st investorld(0) Is taken from 0 to 1-Tl1(0) Wherein D is more than or equal to 2 and less than or equal to D;
and step 3: calculating the fitness value of each tree in turn, and recording the fitness value as Fit (0) ═ F1(0)…Fl(0)…FN(0) In which Fl(0) Representing the fitness value of the first tree in the initial population, recording the individual with the largest fitness value of the current population as the current best solution B, and setting the current iteration time it to be 1;
and 4, step 4: the tree serial number l considered before is 1;
and 5: randomly setting the number SN of seeds of the first tree, belonging to [ 10% N, 25% N ];
step 6: let the currently considered seed sequence number sn ═ 1;
and 7: let d be 1;
and 8: the tree generates offspring tree species according to a rule comprising: migrating to random parent individuals in the population, migrating to optimal parent individuals in the population, and migrating to the direction of random positions;
and step 9: judging whether D is not greater than D, if so, changing D to D +1 and going to step 8;
step 10: sequentially judging whether the values from the 2 nd dimension to the D th dimension of the sn-th seed are less than or equal to 1-Tl1(it), if not, decreasing Tl1(it) such that the 2 nd to D nd dimension values of the sn-th seed are less than or equal to 1-Tl1(it) and calculating the fitness value of the sn-th seed;
step 11: judging whether SN is not greater than SN, if so, making SN ═ SN +1 and going to step 7;
step 12: judging whether the fitness value of the optimal seed of the first tree is smaller than that of the first tree or not, and if so, replacing the position of the first tree with the position of the seed;
step 13: judging whether l is not more than N, if so, changing l to l +1 and going to the step 5;
step 14: updating the current best solution B;
step 15: judging whether the current algorithm running time is not greater than T, if so, turning it to it +1 and turning to the step 4;
step 16: and outputting the current optimal solution B as an optimal carbon information disclosure scheme.
2. The method of claim 1, wherein step 8 comprises:
if rand < ST1, let Slsnd(it)＝Tld(it-1)+α(Bd-Tld(it-1))，Slsnd(it) represents the d-dimensional position of the sn-th seed of the first tree in the it-th generation population;
if it is
Then order Slsnd(it)＝Tld(it-1)+α(Trd(it-1)-Tld(it-1))；
If it is
Then order Slsnd(it) ═ Rand (), where Rand is random number, α is scaling factor, Rand () is random position, all the values are between 0 and 1, t is current running time of algorithm, B isdD-dimensional position, T, representing an optimal solutionrdRepresents any individual in the population other than tree ld-dimensional position.
3. An apparatus for optimizing a carbon information disclosure scheme, the apparatus for determining a degree of carbon information disclosed by a business to individual investors, the apparatus comprising:
a first initialization module, configured to perform step 1: initializing algorithm parameters, including: the method comprises the following steps of (1) counting the population POP, searching trend control parameters ST1 and ST2, problem dimension D and algorithm iteration termination time T, wherein D is N + 1;
a second initialization module, configured to perform step 2: initializing the initial position of the POP tree in the D-dimensional solution space, and recording the initial position as POP (0) { T } T1(0)…Tl(0)…TN(0) In which T isl(0) Denotes the first tree in the initial population, Tl(0)＝{Tl1(0)…Tld(0)…TlD(0) In which T isl1(0) Coefficient, T, representing the degree of basic carbon information revealed by the business to each investorld(0) Degree coefficient, T, representing proprietary carbon information revealed by the business to the d-1 st investorld(0) Is taken from 0 to 1-Tl1(0) Wherein D is more than or equal to 2 and less than or equal to D;
a fitness calculation module for executing step 3: calculating the fitness value of each tree in turn, and recording the fitness value as Fit (0) ═ F1(0)…Fl(0)…FN(0) In which Fl(0) Representing the fitness value of the first tree in the initial population, recording the individual with the largest fitness value of the current population as the current best solution B, and setting the current iteration time it to be 1;
a first setting module, configured to perform step 4: the tree serial number l considered before is 1;
a second setting module, configured to perform step 5: randomly setting the number SN of seeds of the first tree, belonging to [ 10% N, 25% N ];
a third setting module, configured to perform step 6: let the currently considered seed sequence number sn ═ 1;
a fourth setting module, configured to perform step 7: let d be 1;
a generating module for executing step 8: the tree generates offspring tree species according to a rule comprising: migrating to random parent individuals in the population, migrating to optimal parent individuals in the population, and migrating to the direction of random positions;
a first determining module, configured to perform step 9: judging whether D is not greater than D, if so, changing D to D +1 and going to step 8;
a second determining module, configured to perform step 10: sequentially judging whether the values from the 2 nd dimension to the D th dimension of the sn-th seed are less than or equal to 1-Tl1(it), if not, decreasing Tl1(it) such that the 2 nd to D nd dimension values of the sn-th seed are less than or equal to 1-Tl1(it) and calculating the fitness value of the sn-th seed;
a third determining module, configured to perform step 11: judging whether SN is not greater than SN, if so, making SN ═ SN +1 and going to step 7;
a fourth determining module, configured to perform step 12: judging whether the fitness value of the optimal seed of the first tree is smaller than that of the first tree or not, and if so, replacing the position of the first tree with the position of the seed;
a fifth judging module, configured to execute step 13: judging whether l is not more than N, if so, changing l to l +1 and going to the step 5;
an update module for performing step 14: updating the current best solution B;
a sixth determining module, configured to perform step 15: judging whether the current algorithm running time is not greater than T, if so, turning it to it +1 and turning to the step 4;
an output module for performing step 16: and outputting the current optimal solution B as an optimal carbon information disclosure scheme.
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