CN113421171A - MOEA/D evolution multi-objective optimization-based path navigation system and method - Google Patents
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Abstract
The invention provides a MOEA/D evolution multi-objective optimization-based path navigation system and a method thereof, comprising the following steps: identifying ground objects according to the aerial survey image and generating an attribute data set with engineering influence factors; reading initial information of line specifications in an attribute data set with engineering influence factors; generating a corresponding path navigation scheme according to a plurality of preset target trends by utilizing an MOEA/D multi-objective optimization algorithm; optimizing and verifying the path navigation scheme through an iterative algorithm, and confirming the result of the path navigation scheme; selecting and revising a path navigation scheme according to a preset survey information map; and outputting a final scheme of the route planning according to the revised path navigation scheme. The method can automatically realize the multi-target-based power grid line corridor path navigation planning on the basis of the attribute data set with the engineering influence factors.
Description
Technical Field
The invention relates to the technical field of power grid planning design, in particular to a MOEA/D evolution multi-objective optimization-based path navigation system and method.
Background
At present, for planning and designing of a power grid, a remote surveying mode based on an unmanned aerial vehicle field aerial survey image is generally adopted, then a professional is required to manually identify ground objects in a remote sensing image acquired each time, and manual investigation and design are carried out to obtain a final path navigation schematic diagram.
However, with the continuous expansion of the engineering scale, the manual identification, judgment and design inevitably leads to the occurrence of ground object identification errors and unreasonable path planning, so that the final path navigation schematic diagram cannot be implemented, and further the subsequent construction is adversely affected. In addition, along with the improvement of the construction requirement, the path navigation of the power grid also needs to comprehensively consider factors such as path length, special road sections, investment, construction, operation, safety and the like, and the path navigation finished according to professional design specifications only by the experience of designers currently has huge workload and inevitably has errors.
In addition, in the design discussion stage, a design solution providing various target tendencies is needed, and as a contrast design navigation solution, the design solution cannot be realized by the existing manual design method.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a route navigation system and a route navigation method based on MOEA/D evolution multi-objective optimization, which can automatically realize power grid line corridor route navigation planning based on multiple objectives on the basis of an attribute data set with engineering influence factors.
In order to achieve the purpose, the invention is realized by the following technical scheme: a path navigation system based on MOEA/D evolution multi-objective optimization comprises:
the data preparation unit is used for carrying out ground object identification according to the aerial survey image and generating an attribute data set with engineering influence factors;
the reading unit is used for reading the initial information of the line specification in the attribute data set with the engineering influence factor;
the system comprises a scheme generating unit, a route navigation unit and a route navigation unit, wherein the scheme generating unit is used for generating corresponding route navigation schemes according to a plurality of preset target trends by utilizing an MOEA/D multi-objective optimization algorithm;
the scheme confirming unit is used for optimizing and verifying the path navigation scheme through an iterative algorithm and confirming the result of the path navigation scheme;
a selection modification unit for selecting and revising the path navigation scheme according to a preset survey information map;
and the output unit is used for outputting the final scheme of the route planning according to the revised path navigation scheme.
Correspondingly, the invention also discloses a MOEA/D evolution multi-objective optimization-based path navigation method, which comprises the following steps:
s1: identifying ground objects according to the aerial survey image and generating an attribute data set with engineering influence factors;
s2: reading initial information of line specifications in an attribute data set with engineering influence factors;
s3: generating a corresponding path navigation scheme according to a plurality of preset target trends by utilizing an MOEA/D multi-objective optimization algorithm;
s4: optimizing and verifying the path navigation scheme through an iterative algorithm, and confirming the result of the path navigation scheme;
s5: selecting and revising a path navigation scheme according to a preset survey information map;
s6: and outputting a final scheme of the route planning according to the revised path navigation scheme.
Further, the preset target tendency includes: route, construction cost and construction period.
Further, the MOEA/D multi-objective optimization algorithm comprises the following steps:
s31: establishing an initialization population according to the initial information of the line specification;
s32: distributing a weight vector to each subproblem according to a preset target tendency;
s33: each sub-question is cross-mutated in its neighbor individuals;
s34: and updating the parent population according to a preset aggregation function value.
Further, the iterative algorithm comprises the steps of:
s41: randomly generating an initial population of a determined length;
s42: iteratively performing the calculation of fitness values, chromosome replication, crossover and mutation on the cluster population to produce a next generation population;
s43: designating the best individual appearing in any generation as the result of the algorithm execution;
s44: and after circulating the preset genetic algebra, obtaining an optimal result as a solution of process optimization by comparing all algorithm execution results.
Further, the line specification initial information includes: starting point position information of the route, end point position information of the route, and position information of the special route.
Further, the iterative algorithm adopts a multi-target particle swarm algorithm.
Further, the multi-target particle swarm algorithm comprises the following steps:
s81: setting control parameters, group scale and iteration times of a multi-target particle swarm algorithm, and inputting initial information of line specifications;
s82: calculating a target value of a network extension scheme represented by each particle according to the direct current power flow model;
s83: updating the individual optimal value of each particle according to the Pareto optimal concept;
s84: selecting the particles with the minimum sequence value in the current population according to the sequence values of the particles, storing the particles into an external archive, and deleting overload non-inferior solutions in the particles;
s85: updating the speed and the position of each particle according to a preset particle updating formula;
s86: judging whether the iteration times are reached currently, if so, outputting non-inferior solutions in an external archive, otherwise, turning to the step S82;
s87: and checking the non-inferior solution in the external archive and outputting a corresponding network planning diagram.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a MOEA/D evolution multi-objective optimization-based path navigation system and a method, wherein a multi-objective optimization mode is adopted to convert a power design literal standard specification without relevance and logicality into an intelligent design algorithm capable of being graphically displayed; factors such as path length, special road sections, investment, construction, operation, safety and the like are comprehensively considered, and on the basis of an engineering influence factor data set, the design schemes of site selection and routing of the electric power tower with various target tendencies are deduced by referring to expert knowledge, so that the design navigation of the power grid line corridor engineering of the electric power system is realized. On the basis of accurate survey and accurate identification, a contrast design navigation scheme can be provided based on different target tendencies such as shortest distance, fastest construction period, optimal cost and the like.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a system block diagram of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
The first embodiment is as follows:
as shown in fig. 1, the present embodiment provides a route guidance system based on MOEA/D evolutionary multi-objective optimization, including:
and the data preparation unit is used for carrying out ground object identification according to the aerial survey image and generating an attribute data set with the engineering influence factor.
And the reading unit is used for reading the initial information of the line specification in the attribute data set with the engineering influence factor.
And the scheme generating unit is used for generating a corresponding path navigation scheme according to a plurality of preset target trends by utilizing an MOEA/D multi-objective optimization algorithm.
And the scheme confirming unit is used for optimizing and verifying the path navigation scheme through an iterative algorithm and confirming the result of the path navigation scheme.
And the selection modification unit is used for selecting and revising the path navigation scheme according to the preset survey information map.
And the output unit is used for outputting the final scheme of the route planning according to the revised path navigation scheme.
Example two:
correspondingly, as shown in fig. 2, the embodiment also discloses a route navigation method based on MOEA/D evolution multi-objective optimization, which includes the following steps:
s1: and carrying out ground object identification according to the aerial survey image, and generating an attribute data set with engineering influence factors.
S2: and reading initial information of the line specification in the attribute data set with the engineering influence factor.
S3: and generating a corresponding path navigation scheme according to a plurality of preset target trends by utilizing an MOEA/D multi-objective optimization algorithm.
Wherein the preset target tendency comprises: route, construction cost and construction period. Therefore, different target tendencies such as shortest route, shortest construction period, and optimal cost can be set according to the needs, or any combination of the three target tendencies can be set.
The MOEA/D multi-objective optimization algorithm comprises the following steps:
1. and establishing an initialization population according to the initial information of the line specification.
2. Each sub-question is assigned a weight vector according to a preset target tendency.
3. Each sub-question is cross-mutated within its neighbor individuals.
4. And updating the parent population according to a preset aggregation function value.
S4: optimizing and verifying the path navigation scheme through an iterative algorithm, and confirming the result of the path navigation scheme.
The iterative algorithm comprises the following steps: firstly, randomly generating an initial population with a determined length; then, iteratively performing the calculation of fitness values, chromosome replication, crossover and mutation on the cluster population to generate a next generation population; designating the best individual appearing in any generation as the result of the algorithm execution; and after circulating the preset genetic algebra, obtaining an optimal result as a solution of process optimization by comparing all algorithm execution results.
S5: the path navigation scheme is selected and revised based on a preset survey information map.
S6: and outputting a final scheme of the route planning according to the revised path navigation scheme.
Example three:
based on the second embodiment, the embodiment also discloses a path navigation method based on MOEA/D evolution multi-objective optimization, wherein the iterative algorithm adopts a multi-objective particle swarm algorithm, and the method specifically comprises the following steps:
step 1: and setting control parameters, group scale and iteration times of the multi-target particle swarm algorithm, and inputting initial information of line specifications.
Step 2: and calculating a target value of the network expansion scheme represented by each particle according to the direct current power flow model.
And step 3: and updating the individual optimal value of each particle according to the Pareto optimal concept.
And 4, step 4: and selecting the particles with the minimum sequence value in the current population according to the sequence values of the particles, storing the particles into an external archive, and deleting the overload non-inferior solution in the particles.
And 5: and updating the speed and the position of each particle according to a preset particle updating formula.
Step 6: and (4) judging whether the iteration times are reached currently, if so, outputting non-inferior solutions in an external archive, and otherwise, turning to the step 2.
And 7: and checking the non-inferior solution in the external archive and outputting a corresponding network planning diagram.
The MOEA/D evolution multi-objective optimization-based path navigation method comprehensively considers the distance, the construction period, the cost and the line design specification and standard, adopts a multi-objective optimization algorithm to realize path planning, automatic pole distribution and calculation statistics, and realizes the automatic design of the power grid line. The evolutionary algorithm adopted by the embodiment realizes global search by maintaining the population consisting of potential solutions between generations, and the method from the population to the population is very effective for searching Pareto optimal solution sets of the multi-objective optimization problem. Particle swarm optimization, an artificial immune system, a distribution estimation algorithm and the like are inspired by a natural system, and an evolutionary algorithm can be well combined with a multi-objective optimization problem. The evolutionary multi-objective optimization method can effectively solve the high-dimensional multi-objective optimization problem by adopting models such as artificial immunity and particle swarm.
In the embodiments provided by the present invention, it should be understood that the disclosed system, system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit.
The invention is further described with reference to the accompanying drawings and specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and these equivalents also fall within the scope of the present application.
Claims (8)
1. A path navigation system based on MOEA/D evolution multi-objective optimization is characterized by comprising:
the data preparation unit is used for carrying out ground object identification according to the aerial survey image and generating an attribute data set with engineering influence factors;
the reading unit is used for reading the initial information of the line specification in the attribute data set with the engineering influence factor;
the system comprises a scheme generating unit, a route navigation unit and a route navigation unit, wherein the scheme generating unit is used for generating corresponding route navigation schemes according to a plurality of preset target trends by utilizing an MOEA/D multi-objective optimization algorithm;
the scheme confirming unit is used for optimizing and verifying the path navigation scheme through an iterative algorithm and confirming the result of the path navigation scheme;
a selection modification unit for selecting and revising the path navigation scheme according to a preset survey information map;
and the output unit is used for outputting the final scheme of the route planning according to the revised path navigation scheme.
2. A path navigation method based on MOEA/D evolution multi-objective optimization is characterized by comprising the following steps:
s1: identifying ground objects according to the aerial survey image and generating an attribute data set with engineering influence factors;
s2: reading initial information of line specifications in an attribute data set with engineering influence factors;
s3: generating a corresponding path navigation scheme according to a plurality of preset target trends by utilizing an MOEA/D multi-objective optimization algorithm;
s4: optimizing and verifying the path navigation scheme through an iterative algorithm, and confirming the result of the path navigation scheme;
s5: selecting and revising a path navigation scheme according to a preset survey information map;
s6: and outputting a final scheme of the route planning according to the revised path navigation scheme.
3. The MOEA/D evolution multi-objective optimization based path navigation method of claim 2, wherein the preset objective tendency comprises: route, construction cost and construction period.
4. The MOEA/D evolution multi-objective optimization-based path navigation method according to claim 2, wherein the MOEA/D multi-objective optimization algorithm comprises the following steps:
s31: establishing an initialization population according to the initial information of the line specification;
s32: distributing a weight vector to each subproblem according to a preset target tendency;
s33: each sub-question is cross-mutated in its neighbor individuals;
s34: and updating the parent population according to a preset aggregation function value.
5. The MOEA/D evolution multi-objective optimization based path navigation method according to claim 2, wherein the iterative algorithm comprises the following steps:
s41: randomly generating an initial population of a determined length;
s42: iteratively performing the calculation of fitness values, chromosome replication, crossover and mutation on the cluster population to produce a next generation population;
s43: designating the best individual appearing in any generation as the result of the algorithm execution;
s44: and after circulating the preset genetic algebra, obtaining an optimal result as a solution of process optimization by comparing all algorithm execution results.
6. The MOEA/D evolution multi-objective optimization based path navigation method of claim 2, wherein the initial route specification information comprises: starting point position information of the route, end point position information of the route, and position information of the special route.
7. The MOEA/D evolution multi-objective optimization based path navigation method according to claim 2, wherein the iterative algorithm adopts a multi-objective particle swarm algorithm.
8. The MOEA/D evolution multi-objective optimization-based path navigation method according to claim 7, wherein the multi-objective particle swarm algorithm comprises the following steps:
s81: setting control parameters, group scale and iteration times of a multi-target particle swarm algorithm, and inputting initial information of line specifications;
s82: calculating a target value of a network extension scheme represented by each particle according to the direct current power flow model;
s83: updating the individual optimal value of each particle according to the Pareto optimal concept;
s84: selecting the particles with the minimum sequence value in the current population according to the sequence values of the particles, storing the particles into an external archive, and deleting overload non-inferior solutions in the particles;
s85: updating the speed and the position of each particle according to a preset particle updating formula;
s86: judging whether the iteration times are reached currently, if so, outputting non-inferior solutions in an external archive, otherwise, turning to the step S82;
s87: and checking the non-inferior solution in the external archive and outputting a corresponding network planning diagram.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108647607A (en) * | 2018-04-28 | 2018-10-12 | 国网湖南省电力有限公司 | Objects recognition method for project of transmitting and converting electricity |
| CN110039540A (en) * | 2019-05-27 | 2019-07-23 | 聊城大学 | A kind of service robot paths planning method that multiple target optimizes simultaneously |
| CN111709571A (en) * | 2020-06-09 | 2020-09-25 | 吉林大学 | Ship collision avoidance route determining method, device, equipment and storage medium |
| CN111768033A (en) * | 2020-06-28 | 2020-10-13 | 广东电网有限责任公司 | Multi-target alternating current/direct current power distribution network planning method and device |
| CN111815016A (en) * | 2020-05-22 | 2020-10-23 | 云南电网有限责任公司信息中心 | Power transmission line path optimization method and computer program product |
| CN112327923A (en) * | 2020-11-19 | 2021-02-05 | 中国地质大学（武汉） | Multi-unmanned aerial vehicle collaborative path planning method |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108647607A (en) * | 2018-04-28 | 2018-10-12 | 国网湖南省电力有限公司 | Objects recognition method for project of transmitting and converting electricity |
| CN110039540A (en) * | 2019-05-27 | 2019-07-23 | 聊城大学 | A kind of service robot paths planning method that multiple target optimizes simultaneously |
| CN111815016A (en) * | 2020-05-22 | 2020-10-23 | 云南电网有限责任公司信息中心 | Power transmission line path optimization method and computer program product |
| CN111709571A (en) * | 2020-06-09 | 2020-09-25 | 吉林大学 | Ship collision avoidance route determining method, device, equipment and storage medium |
| CN111768033A (en) * | 2020-06-28 | 2020-10-13 | 广东电网有限责任公司 | Multi-target alternating current/direct current power distribution network planning method and device |
| CN112327923A (en) * | 2020-11-19 | 2021-02-05 | 中国地质大学（武汉） | Multi-unmanned aerial vehicle collaborative path planning method |
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