CN113487142A - Evolution optimization method and system for E-government performance assessment management - Google Patents
Evolution optimization method and system for E-government performance assessment management Download PDFInfo
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Abstract
The invention discloses an evolution optimization method for E-government performance assessment management, which comprises the following steps: constructing a chromosome formalized random vector initial population of the E-government performance assessment indexes; calculating a fitness function value according to the scoring completion condition, the importance weight and the special priority setting value of the E-government performance assessment index; sequencing the chromosome vectors of the E-government performance assessment indexes according to the fitness function values, and judging whether the optimal chromosome vectors meet the optimization target; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target. The method can promote the efficiency improvement and the intellectualization of the E-government performance assessment management.
Description
Technical Field
The invention relates to the technical field of electronic government affair management, in particular to an evolution optimization method and system for electronic government affair performance assessment management.
Background
Because the E-government performance assessment management has stronger speciality, policy variability, unknown property and space-time limitation, the labor cost of field management is higher, and more uncertain factors exist, the efficient pushing of assessment tasks and the optimization of assessment scoring sequence problems of the E-government performance assessment management are always difficult points.
Most of traditional electronic government affair services can not efficiently push assessment tasks and optimize assessment scoring sequence problems of electronic government affair performance assessment management, and belong to a non-bionic conventional programming method.
Disclosure of Invention
Objects of the invention
The invention aims to provide an evolution optimization method and system for E-government performance assessment management, which aim to solve the problem that the existing E-government software is difficult to efficiently push assessment tasks and optimize assessment scoring order service.
(II) technical scheme
To solve the above problems, a first aspect of the present invention provides an evolutionary optimization method for e-government performance assessment management, including: constructing a chromosome formalized random vector initial population of the E-government performance assessment indexes; calculating a fitness function value according to the scoring completion condition, the importance weight and the special priority setting value of the E-government performance assessment index; sequencing the chromosome vectors of the E-government performance assessment indexes according to the fitness function values, and judging whether the optimal chromosome vectors meet the optimization target; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
Further, the step of constructing the chromosome-formalized random vector initial population of the E-government performance assessment indexes comprises the following steps of: inputting the name of the E-government performance assessment index; inputting a value range of the E-government performance assessment index; inputting an importance weight of an electronic government performance assessment index; inputting a scoring standard of an electronic government performance assessment index; inputting a special priority setting value of the scoring order of the electronic government performance assessment indexes; recording the priority random initial ranking number of the E-government performance assessment index; and inputting a scoring completion progress value of the E-government performance assessment index.
Further, the step of calculating the fitness function value according to the scoring completion condition, the importance weight and the special priority setting value of the E-government performance assessment index comprises the following steps: the priority of the E-government performance assessment indexes which are scored is set to be lower than that of the E-government performance assessment indexes which are not scored, the E-government performance assessment indexes which are scored are sorted according to the initial sequence numbers of the indexes, and fitness function values are set; the larger the importance weight value in the E-government performance assessment indexes which are not scored is, the higher the priority setting is, and the larger the fitness function value is; and calculating the fitness function value according to the special priority setting value under the condition that other conditions are equal.
Further, the chromosome vectors of the E-government performance assessment indexes are sequenced according to the fitness function values, and whether the optimal chromosome vector meets an optimization target is judged; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
According to another aspect of the present invention, there is provided an evolutionary optimization system for e-government performance assessment management, comprising: the chromosome formal random vector initial population construction module of the e-government performance assessment index is used for constructing a chromosome formal random vector initial population of the e-government performance assessment index; the fitness function value calculating module is used for calculating a fitness function value according to the grading completion condition, the importance weight and the special priority setting value of the electronic government performance assessment index; the chromosome vector sorting and optimization target judging module is used for sorting the chromosome vectors of the E-government performance assessment indexes according to the fitness function value and judging whether the optimal chromosome vector meets the optimization target or not; and the chromosome vector mutation cross operation module is used for generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector.
Further, the chromosome formalized random vector initial population construction module of the e-government performance assessment index comprises: the name input unit of the electronic government affair performance examination index is used for inputting the name of the electronic government affair performance examination index; the value range input unit of the electronic government affair performance assessment index is used for inputting the value range of the electronic government affair performance assessment index; the importance weight recording unit is used for recording the importance weight of the electronic government performance assessment index; the scoring standard input unit of the electronic government affair performance assessment index is used for inputting the scoring standard of the electronic government affair performance assessment index; the special priority setting value recording unit is used for recording the special priority setting value of the scoring order of the electronic government affair performance assessment indexes; the priority random initial ranking number recording unit of the electronic government affair performance assessment indexes is used for recording the priority random initial ranking numbers of the electronic government affair performance assessment indexes; and the scoring completion progress value recording unit is used for recording the scoring completion progress value of the electronic government affair performance assessment index.
Further, the fitness function value calculating module includes: setting a fitness function value unit for the indexes which are graded, wherein the fitness function value unit is used for setting fitness function values for the indexes which are graded, the priority of the E-government performance assessment indexes which are graded is set to be lower than the priority of the E-government performance assessment indexes which are not graded, and the E-government performance assessment indexes which are graded are sorted according to the initial sequence numbers of the indexes; a fitness function value unit is set for the non-scored indexes and is used for setting fitness function values for the non-scored indexes, and the higher the importance weight value in the non-scored e-government performance assessment indexes is, the higher the priority setting is, the larger the fitness function value is; and the special priority setting unit is used for setting the fitness function value according to the special priority setting value under the condition that other conditions are equal.
The system further comprises a chromosome vector variation cross operation module, a fitness function value calculation module and a judgment module, wherein the chromosome vector variation cross operation module is used for sequencing chromosome vectors of the E-government performance assessment indexes according to the fitness function value and judging whether the optimal chromosome vector meets an optimization target; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
(III) advantageous effects
The technical scheme of the invention has the following beneficial technical effects:
by the method and the system, the complex situations related to intellectualization and optimization in the E-government performance assessment management can be evolved and optimized, targeted assessment task pushing, intelligent management and optimization are set according to the scoring conditions and the priorities of different situations, and efficient and intelligent operation of the E-government performance assessment management is guaranteed.
Drawings
FIG. 1 is a flow chart of a method for evolutionary optimization of E-government performance assessment management in accordance with a first embodiment of the present invention;
FIG. 2 is a schematic diagram of an evolutionary optimization system for E-government performance assessment management in accordance with an alternative embodiment of the present invention;
fig. 3 is a flow chart of an evolutionary optimization algorithm for e-government performance assessment management in an alternative embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all 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 addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, in a first aspect of the embodiment of the invention, there is provided an evolutionary optimization method for e-government performance assessment management, including: constructing a chromosome formalized random vector initial population of the E-government performance assessment indexes; calculating a fitness function value according to the scoring completion condition, the importance weight and the special priority setting value of the E-government performance assessment index; sequencing the chromosome vectors of the E-government performance assessment indexes according to the fitness function values, and judging whether the optimal chromosome vectors meet the optimization target; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
The method of the embodiment can perform evolution optimization of electronic government performance assessment management on the problem that the conventional electronic government software is difficult to efficiently push assessment tasks and optimize assessment scoring sequence service, and targeted assessment task pushing, intelligent management and optimization are set according to scoring conditions and priorities under different conditions, so that efficient and intelligent operation of electronic government performance assessment management is guaranteed.
Optionally, the step of constructing a chromosome-formalized random vector initial population of the e-government performance assessment index includes: inputting the name of the E-government performance assessment index; inputting a value range of the E-government performance assessment index; inputting an importance weight of an electronic government performance assessment index; inputting a scoring standard of an electronic government performance assessment index; inputting a special priority setting value of the scoring order of the electronic government performance assessment indexes; recording the priority random initial ranking number of the E-government performance assessment index; and inputting a scoring completion progress value of the E-government performance assessment index.
Optionally, the step of calculating the fitness function value according to the scoring completion condition, the importance weight and the special priority setting value of the e-government performance assessment index includes: the priority of the E-government performance assessment indexes which are scored is set to be lower than that of the E-government performance assessment indexes which are not scored, the E-government performance assessment indexes which are scored are sorted according to the initial sequence numbers of the indexes, and fitness function values are set; the larger the importance weight value in the E-government performance assessment indexes which are not scored is, the higher the priority setting is, and the larger the fitness function value is; and calculating the fitness function value according to the special priority setting value under the condition that other conditions are equal.
Optionally, the chromosome vectors of the e-government performance assessment indicators are sorted according to the fitness function values, and whether the optimal chromosome vector meets an optimization target is judged; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
In another aspect of an embodiment of the present invention, there is provided an evolutionary optimization system for e-government performance assessment management, including: the chromosome formal random vector initial population construction module of the e-government performance assessment index is used for constructing a chromosome formal random vector initial population of the e-government performance assessment index; the fitness function value calculating module is used for calculating a fitness function value according to the grading completion condition, the importance weight and the special priority setting value of the electronic government performance assessment index; the chromosome vector sorting and optimization target judging module is used for sorting the chromosome vectors of the E-government performance assessment indexes according to the fitness function value and judging whether the optimal chromosome vector meets the optimization target or not; and the chromosome vector mutation cross operation module is used for generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector.
Optionally, the chromosome-formalized random vector initial population construction module for the e-government performance assessment index includes: the name input unit of the electronic government affair performance examination index is used for inputting the name of the electronic government affair performance examination index; the value range input unit of the electronic government affair performance assessment index is used for inputting the value range of the electronic government affair performance assessment index; the importance weight recording unit is used for recording the importance weight of the electronic government performance assessment index; the scoring standard input unit of the electronic government affair performance assessment index is used for inputting the scoring standard of the electronic government affair performance assessment index; the special priority setting value recording unit is used for recording the special priority setting value of the scoring order of the electronic government affair performance assessment indexes; the priority random initial ranking number recording unit of the electronic government affair performance assessment indexes is used for recording the priority random initial ranking numbers of the electronic government affair performance assessment indexes; and the scoring completion progress value recording unit is used for recording the scoring completion progress value of the electronic government affair performance assessment index.
Optionally, the fitness function value calculating module includes: setting a fitness function value unit for the indexes which are graded, wherein the fitness function value unit is used for setting fitness function values for the indexes which are graded, the priority of the E-government performance assessment indexes which are graded is set to be lower than the priority of the E-government performance assessment indexes which are not graded, and the E-government performance assessment indexes which are graded are sorted according to the initial sequence numbers of the indexes; a fitness function value unit is set for the non-scored indexes and is used for setting fitness function values for the non-scored indexes, and the higher the importance weight value in the non-scored e-government performance assessment indexes is, the higher the priority setting is, the larger the fitness function value is; and the special priority setting unit is used for setting the fitness function value according to the special priority setting value under the condition that other conditions are equal.
Optionally, the system further comprises a chromosome vector variation cross operation module, configured to sort the chromosome vectors of the e-government performance assessment indicators according to the fitness function value, and determine whether the optimal chromosome vector meets an optimization target; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
In an optional embodiment, an evolutionary optimization algorithm for E-government performance assessment management is provided, and the evolutionary optimization system for E-government performance assessment management is constructed according to the following steps.
1. Evolution optimization system for constructing electronic government performance assessment management
The evolution optimization system for the E-government performance assessment management is composed of a chromosome formalized random vector initial population construction module, a fitness function value calculation module, a chromosome vector sorting and optimization target judgment module and a chromosome vector variation cross operation module of the E-government performance assessment indexes, wherein the fitness function value calculation module, the chromosome vector sorting and optimization target judgment module and the chromosome vector variation cross operation module are evolution calculation centers of the evolution optimization system for the E-government performance assessment management, and are shown in figure 2. The evolution optimization algorithm of the electronic government performance assessment management is a core optimization algorithm of an evolution optimization system of the electronic government performance assessment management, and mainly comprises the following steps: initializing the population randomly; calculating a fitness function value; optimizing and selecting operation; performing mutation operation; the crossover operation and the iterative decision and calculation operation are shown in fig. 3.
2. Constructing randomly initialized populations
When an evolution optimization system for E-government performance assessment management is constructed, a chromosome formal random vector initial population construction module of a necessary E-government performance assessment index is constructed firstly, and the chromosome formal random vector initial population construction module comprises a name entry unit of an E-government performance examination index, a value range entry unit of the E-government performance assessment index, an importance weight entry unit of the E-government performance assessment index, a grading standard entry unit of the E-government performance assessment index, a special priority setting value entry unit of a grading order of the E-government performance assessment index, a priority random initial sequencing number entry unit of the E-government performance assessment index and a grading completion progress value entry unit of the E-government performance assessment index.
And the name input unit of the electronic government affair performance examination index is used for inputting the name of the electronic government affair performance examination index.
And the value range input unit of the electronic government affair performance assessment index is used for inputting the value range of the electronic government affair performance assessment index.
And the importance weight value recording unit is used for recording the importance weight value of the electronic government performance assessment index.
And the scoring standard input unit of the electronic government affair performance assessment index is used for inputting the scoring standard of the electronic government affair performance assessment index.
And the special priority setting value recording unit is used for recording the special priority setting value of the scoring order of the electronic government affair performance assessment indexes.
And the priority random initial ranking number recording unit of the electronic government affair performance assessment indexes is used for recording the priority random initial ranking numbers of the electronic government affair performance assessment indexes.
And the scoring completion progress value recording unit is used for recording the scoring completion progress value of the electronic government performance assessment index.
3. Constructing optimal selection operator
And sequencing according to fitness function values of the chromosome vector individuals in the population, and selecting the individual with the maximum fitness function value as an optimal individual and the electronic government affair performance assessment index with the highest grading priority. And (4) carrying out final elimination on the individuals with the worst fitness function values to complete the optimization selection operation of the population. The priority of the E-government performance assessment indexes which are scored is set to be lower than that of the E-government performance assessment indexes which are not scored, and the E-government performance assessment indexes which are scored are sorted according to the initial sequence numbers of the indexes to set fitness function values. The larger the importance weight value in the E-government performance assessment indexes which are not scored is, the higher the priority setting is, and the larger the fitness function value is. And calculating the fitness function value according to the special priority setting value under the condition that other conditions are equal.
4. Constructing mutation operators
Randomly extracting chromosome vector individuals in the population, randomly mutating the priority ranking numbers of the chromosome vector individuals to generate new chromosome vector individuals, and completing the mutation operation of the population.
5. Constructing cross-operation operators
Randomly extracting two chromosome vector individuals in the population, interchanging the priority sequence numbers of the two chromosome vector individuals to generate two new chromosome vector individuals, and finishing the cross operation of the population.
The above is just one example, and the evolution optimization flow of the e-government performance assessment management is shown in fig. 1. The evolution optimization system for the E-government performance assessment management adopted by the example can be popularized to other E-government systems and is used for realizing intelligent management and optimization of the E-government systems.
The invention aims to protect an evolution optimization method for E-government performance assessment management, which comprises the following steps: constructing a chromosome formalized random vector initial population of the E-government performance assessment indexes; calculating a fitness function value according to the scoring completion condition, the importance weight and the special priority setting value of the E-government performance assessment index; sequencing the chromosome vectors of the E-government performance assessment indexes according to the fitness function values, and judging whether the optimal chromosome vectors meet the optimization target; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (8)
1. An evolution optimization method for E-government performance assessment management is characterized by comprising the following steps:
constructing a chromosome formalized random vector initial population of the E-government performance assessment indexes;
calculating a fitness function value according to the scoring completion condition, the importance weight and the special priority setting value of the E-government performance assessment index;
sequencing the chromosome vectors of the E-government performance assessment indexes according to the fitness function values, and judging whether the optimal chromosome vectors meet the optimization target;
if the optimal chromosome vector meets the optimization goal, ending the algorithm;
otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector;
judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm;
otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
2. The evolutionary optimization method for e-government performance assessment management according to claim 1, wherein the step of constructing a chromosome-formalized random vector initial population of e-government performance assessment indicators comprises:
inputting the name of the E-government performance assessment index;
inputting a value range of the E-government performance assessment index;
inputting an importance weight of an electronic government performance assessment index;
inputting a scoring standard of an electronic government performance assessment index;
inputting a special priority setting value of the scoring order of the electronic government performance assessment indexes;
recording the priority random initial ranking number of the E-government performance assessment index; and
and inputting a scoring completion progress value of the E-government performance assessment index.
3. The evolutionary optimization method for e-government performance assessment management according to claim 1 or 2, wherein the step of calculating the fitness function value according to the scoring completion condition, the importance weight value and the special priority setting value of the e-government performance assessment index comprises:
the priority of the E-government performance assessment indexes which are scored is set to be lower than that of the E-government performance assessment indexes which are not scored, the E-government performance assessment indexes which are scored are sorted according to the initial sequence numbers of the indexes, and fitness function values are set;
the larger the importance weight value in the E-government performance assessment indexes which are not scored is, the higher the priority setting is, and the larger the fitness function value is; and
and calculating the fitness function value according to the special priority setting value under the condition that other conditions are equal.
4. The evolutionary optimization method for e-government performance assessment management according to claim 3, wherein the chromosome vectors of e-government performance assessment indicators are sorted according to the fitness function values to determine whether the optimal chromosome vector meets the optimization target;
if the optimal chromosome vector meets the optimization goal, ending the algorithm;
otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector;
judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm;
otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
5. An evolutionary optimization system for e-government performance assessment management, comprising:
the chromosome formal random vector initial population construction module of the e-government performance assessment index is used for constructing a chromosome formal random vector initial population of the e-government performance assessment index;
the fitness function value calculating module is used for calculating a fitness function value according to the grading completion condition, the importance weight and the special priority setting value of the electronic government performance assessment index;
the chromosome vector sorting and optimization target judging module is used for sorting the chromosome vectors of the E-government performance assessment indexes according to the fitness function value and judging whether the optimal chromosome vector meets the optimization target or not;
and the chromosome vector mutation cross operation module is used for generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector.
6. The system for evolutionary optimization for e-government performance assessment management according to claim 5, wherein said module for constructing chromosome-formalized random vector initial population of e-government performance assessment indicators comprises:
the name input unit of the electronic government affair performance examination index is used for inputting the name of the electronic government affair performance examination index;
the value range input unit of the electronic government affair performance assessment index is used for inputting the value range of the electronic government affair performance assessment index;
the importance weight recording unit is used for recording the importance weight of the electronic government performance assessment index;
the scoring standard input unit of the electronic government affair performance assessment index is used for inputting the scoring standard of the electronic government affair performance assessment index;
the special priority setting value recording unit is used for recording the special priority setting value of the scoring order of the electronic government affair performance assessment indexes;
the priority random initial ranking number recording unit of the electronic government affair performance assessment indexes is used for recording the priority random initial ranking numbers of the electronic government affair performance assessment indexes; and
and the scoring completion progress value recording unit is used for recording the scoring completion progress value of the electronic government performance assessment index.
7. The immune computation and decision-making system of the enterprise remote intelligent service of claim 5 or 6, wherein the fitness function value calculation module comprises:
setting a fitness function value unit for the indexes which are graded, wherein the fitness function value unit is used for setting fitness function values for the indexes which are graded, the priority of the E-government performance assessment indexes which are graded is set to be lower than the priority of the E-government performance assessment indexes which are not graded, and the E-government performance assessment indexes which are graded are sorted according to the initial sequence numbers of the indexes;
a fitness function value unit is set for the non-scored indexes and is used for setting fitness function values for the non-scored indexes, and the higher the importance weight value in the non-scored e-government performance assessment indexes is, the higher the priority setting is, the larger the fitness function value is;
and the special priority setting unit is used for setting the fitness function value according to the special priority setting value under the condition that other conditions are equal.
8. The system for evolutionary optimization for e-government performance assessment management according to claim 7, further comprising the chromosome vector variation cross operation module for sorting the chromosome vectors of e-government performance assessment indicators according to the fitness function values, and determining whether the optimal chromosome vector meets the optimization objective; if the optimal chromosome vector meets the optimization goal, ending the algorithm; otherwise, generating a new chromosome vector through the mutation operation of the chromosome vector and the cross operation between the two chromosome vectors, adding the new chromosome vector into the current population, calculating the fitness function value of the new chromosome vector, sequencing all the chromosome vectors of the current population according to the fitness function value, and eliminating the last chromosome vector; judging whether the optimal chromosome vector meets the optimization target, and if the optimal chromosome vector meets the optimization target, finishing the algorithm; otherwise, carrying out iterative evolution optimization again through the mutation operation and the cross operation until the optimal chromosome vector meets the optimization target.
Priority Applications (1)
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