WO2017121053A1 - Procédé et système d'évaluation d'un degré de collision entre des cibles - Google Patents

Procédé et système d'évaluation d'un degré de collision entre des cibles Download PDF

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Publication number
WO2017121053A1
WO2017121053A1 PCT/CN2016/081495 CN2016081495W WO2017121053A1 WO 2017121053 A1 WO2017121053 A1 WO 2017121053A1 CN 2016081495 W CN2016081495 W CN 2016081495W WO 2017121053 A1 WO2017121053 A1 WO 2017121053A1
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Prior art keywords
targets
conflict
target
degree
probability information
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PCT/CN2016/081495
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English (en)
Chinese (zh)
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李霞
罗乃丽
王娜
陈泯融
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深圳大学
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Publication of WO2017121053A1 publication Critical patent/WO2017121053A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities

Definitions

  • the present invention relates to the field of information technology, and in particular, to a method and system for evaluating the degree of conflict between targets.
  • Pareto optimal frontier In the fields of engineering, industry and science, the problem of target optimization is often encountered. Modern evolutionary multi-objective optimization algorithms have become the most effective way to deal with such problems. In the multi-objective optimization problem, because of the strong or weak conflict between the targets, the algorithm can not obtain the only one optimal solution as the single-objective optimization problem, but obtain a set of compromises for each target.
  • the optimal solution set usually called the Pareto optimal solution set, is called Pareto optimal frontier in the target space.
  • the solutions do not dominate each other, which is also called each other as a non-dominated solution; the so-called dominating solution y is, a solution x Pareto dominates y, if and only if x is adapted to each target Not worse than y and the fitness at least on one target is strictly better than y.
  • the so-called dominating solution y is, a solution x Pareto dominates y, if and only if x is adapted to each target Not worse than y and the fitness at least on one target is strictly better than y.
  • MOPs multi-objective optimization problems
  • MOOPs Many-Objective Optimization Problems
  • an object of the present invention is to provide a method and system for evaluating the degree of conflict between targets, aiming at solving the problem of low recognition probability in the evolution of high-dimensional multi-objective optimization in the prior art.
  • the present invention proposes a method for assessing the degree of conflict between targets, including:
  • the statistical conflict probability information is used as the degree of conflict between the two targets to quantify the conflict between the targets.
  • the step of defining conflict probability information between two targets includes:
  • the sample data set includes an approximate solution generated using an evolutionary algorithm.
  • the using the sample data set to calculate the conflict probability information between the two targets includes:
  • X is a feasible solution set for a high-dimensional target optimization problem.
  • POP ⁇ x 1 ,..., x N ⁇ , x i ⁇ X
  • k ⁇ l the definition of the permutation combination
  • there is kind of method if there is The following conditions are satisfied for the individual: [f i (x k ) ⁇ f i (x l )] ⁇ [f j (x k ) ⁇ f j (x l )] or [f i (x k ) ⁇ f i (x l )] ⁇ [f j (x k ) ⁇ f j (x l )];
  • the collision probability information of the target f i and the target f j on X can be calculated by the following formula:
  • the present invention also provides a system for assessing the degree of conflict between targets, comprising:
  • a definition module for defining conflict probability information between two targets
  • a statistics module configured to calculate, by using a sample data set, conflict probability information between the two targets
  • a quantization module configured to use the calculated conflict probability information as a conflict degree between the two targets to quantify the conflict between the targets.
  • the definition module is specifically configured to, in a given high-dimensional target optimization problem, set P to map from the target set ⁇ and its own Cartesian product (ie, ⁇ ) to [0, 1]. , that is, P: ⁇ ⁇ ⁇ ⁇ [0, 1], then P is called the collision probability map between the targets, therefore, P(f i , f j ) is called collision probability information between the target f i and the target f j .
  • the sample data set includes an approximate solution generated using an evolutionary algorithm.
  • the technical solution provided by the invention can analyze the conflict information of the target set on the approximate solution set by using the statistical method when dealing with the high-dimensional target optimization problem, and the method can recognize the conflict between the targets and the degree of the conflict with high probability.
  • the recognition rate can reach 100%, and the method is not affected by the Pareto frontier shape, that is, it is not affected by the approximate solution set quality.
  • FIG. 1 is a flow chart of a method for evaluating a degree of conflict between targets in an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a system for evaluating the degree of conflict between targets in an embodiment of the present invention.
  • a specific embodiment of the present invention provides a method for evaluating the degree of conflict between targets, which mainly includes the following steps:
  • the invention adopts a statistical method to analyze the conflict information of the target set on the approximate solution set when dealing with the high-dimensional target optimization problem, which can recognize the conflict between the targets and the degree of the conflict with high probability, with the increase of the sample points.
  • the recognition rate can reach 100%, and the method is not affected by the Pareto frontier shape, that is, it is not affected by the approximate solution set quality.
  • FIG. 1 is a flowchart of a method for evaluating the degree of conflict between targets in an embodiment of the present invention.
  • step S11 conflict probability information between the two targets is defined.
  • the step S11 of defining the conflict probability information between the two targets includes: in a given one-dimensional optimization problem (MOOP), setting P as the target set ⁇ and its own Cartesian product (ie ⁇ ⁇ ⁇ ) map to the map of [0, 1], that is, P: ⁇ ⁇ ⁇ ⁇ [0, 1], then P is called the collision probability map between the targets, therefore, P(f i , f j ) is called collision probability information between the target f i and the target f j .
  • MOOP one-dimensional optimization problem
  • step S12 the conflict probability information between the two targets is counted using the sample data set.
  • the sample data set includes an approximate solution generated by an evolutionary algorithm, where the evolutionary algorithm includes a second generation genetic algorithm (NSGA2), and may include other evolutionary algorithms, and Make a limit.
  • the evolutionary algorithm includes a second generation genetic algorithm (NSGA2), and may include other evolutionary algorithms, and Make a limit.
  • NSGA2 second generation genetic algorithm
  • step S12 of using the sample data set to calculate the conflict probability information between the two targets includes:
  • X is a feasible solution set for a high-dimensional target optimization problem.
  • POP ⁇ x 1 ,..., x N ⁇ , x i ⁇ X
  • k ⁇ l the definition of the permutation combination
  • there is kind of method if there is The following conditions are satisfied for the individual: [f i (x k ) ⁇ f i (x l )] ⁇ [f j (x k ) ⁇ f j (x l )] or [f i (x k ) ⁇ f i ( x l )] ⁇ [f j (x k ) ⁇ f j (x l )];
  • the collision probability information of the target f i and the target f j on X can be calculated by the following formula:
  • the conflict probability information between the two targets in the high-dimensional target optimization problem can be obtained from the above formula.
  • step S13 the statistical conflict probability information is used as the degree of conflict between the two targets to quantify the conflict between the targets.
  • the metric method has objectivity, and the accuracy for identifying the conflicting target according to the metric result is not affected by the quality of the approximate solution set.
  • the invention uses the statistical method to analyze the conflict information of the target set on the approximate solution set when dealing with the high-dimensional target optimization problem, which can analyze the conflict degree between the targets from an objective perspective, and can also identify the target with high probability. Whether the conflict and the degree of conflict, with the increase of sample points, the recognition rate can reach 100%, and the method is not affected by the Pareto frontier shape, that is, it is not affected by the approximate solution set quality.
  • the specific embodiment of the present invention further provides a system 10 for evaluating the degree of conflict between targets, which mainly includes:
  • a definition module 11 for defining conflict probability information between two targets
  • the statistics module 12 is configured to use the sample data set to calculate conflict probability information between the two targets;
  • the quantization module 13 is configured to use the statistical conflict probability information as the conflict degree between the two targets to quantify the conflict between the targets.
  • the invention adopts a statistical method to analyze the conflict information of the target set on the approximate solution set when dealing with the high-dimensional target optimization problem, which can recognize the conflict between the targets and the degree of the conflict with high probability, with the increase of the sample points.
  • the recognition rate can reach 100%, and the method is not affected by the Pareto frontier shape, that is, it is not affected by the approximate solution set quality.
  • the system 10 for assessing the degree of conflict between targets includes a definition module 11, a statistics module 12, and a quantization module 13.
  • a definition module 11 is defined for defining conflict probability information between two targets.
  • the definition module 11 is specifically used to give a Cartesian product from the target set ⁇ and its own in a given Many-Objective Optimization Problem (MOOP) (ie, ⁇ ⁇ ) maps to [0,1], ie P: ⁇ [0,1], then P is called the collision probability map between targets, therefore, P(f i , f j ) is called collision probability information between the target f i and the target f j .
  • MOOP Many-Objective Optimization Problem
  • the statistics module 12 is configured to use the sample data set to calculate conflict probability information between the two targets.
  • the sample data set includes an approximate solution generated by an evolutionary algorithm, where the evolutionary algorithm includes a second generation genetic algorithm (NSGA2), and may include other evolutionary algorithms, and Make a limit.
  • the evolutionary algorithm includes a second generation genetic algorithm (NSGA2), and may include other evolutionary algorithms, and Make a limit.
  • NSGA2 second generation genetic algorithm
  • the conflict probability information between the two targets in the high-dimensional target optimization problem can be obtained from the above formula.
  • the quantization module 13 is configured to use the statistical conflict probability information as the conflict degree between the two targets to quantify the conflict between the targets.
  • the metric method has objectivity, and the accuracy for identifying the conflicting target according to the metric result is not affected by the quality of the approximate solution set.
  • the invention uses the statistical method to analyze the conflict information of the target set on the approximate solution set when dealing with the high-dimensional target optimization problem, which can analyze the conflict degree between the targets from an objective perspective, and can also identify the target with high probability. Whether there is conflict and the degree of conflict, with the increase of sample points, the recognition rate It can reach 100% and it is concluded that the method is not affected by the Pareto frontier shape, ie it is not affected by the approximate solution set quality.
  • each unit included is only divided according to functional logic, but is not limited to the above division, as long as the corresponding function can be implemented; in addition, the specific name of each functional unit is also They are only used to facilitate mutual differentiation and are not intended to limit the scope of the present invention.

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Abstract

L'invention concerne un procédé d'évaluation d'un degré de collision entre des cibles, et, selon ce procédé : des informations de probabilité de collision entre deux cibles sont définies (S11) ; des statistiques concernant les informations de probabilité de collision entre les deux cibles sont calculées au moyen d'un ensemble de données échantillons (S12) ; et les informations de probabilité de collision ayant fait l'objet des statistiques sont considérées comme un degré de collision entre les deux cibles de manière à quantifier une collision entre les cibles (S13). Selon le procédé et un système qui lui correspond, des informations de collision concernant un ensemble de cibles dans un ensemble de solutions approchées sont analysées à l'aide d'une méthode statistique, ce qui permet d'identifier avec une forte probabilité l'éventualité d'une collision entre des cibles et un degré de collision. Avec l'augmentation des points échantillons, le taux d'identification peut atteindre 100 %, et le procédé ne subit pas l'influence de la forme d'un front de Pareto, c'est-à-dire l'influence de la qualité d'un ensemble de solutions approchées.
PCT/CN2016/081495 2016-01-15 2016-05-10 Procédé et système d'évaluation d'un degré de collision entre des cibles WO2017121053A1 (fr)

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CN201610029033.8A CN105719064A (zh) 2016-01-15 2016-01-15 一种评估目标之间冲突程度的方法及其系统
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Citations (3)

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US20060004683A1 (en) * 2004-06-30 2006-01-05 Talbot Patrick J Systems and methods for generating a decision network from text
CN104021392A (zh) * 2014-01-27 2014-09-03 河南大学 一种基于向量度量的冲突证据融合方法
CN104462826A (zh) * 2014-12-11 2015-03-25 云南师范大学 基于矩阵奇异值分解的多传感器证据冲突检测与度量方法

Patent Citations (3)

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US20060004683A1 (en) * 2004-06-30 2006-01-05 Talbot Patrick J Systems and methods for generating a decision network from text
CN104021392A (zh) * 2014-01-27 2014-09-03 河南大学 一种基于向量度量的冲突证据融合方法
CN104462826A (zh) * 2014-12-11 2015-03-25 云南师范大学 基于矩阵奇异值分解的多传感器证据冲突检测与度量方法

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