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Multi-objective optimization method based on similarity measurement

A multi-objective optimization, similarity measurement technology, applied in constraint-based CAD, design optimization/simulation, special data processing applications, etc., can solve problems such as low accuracy and slow algorithm convergence speed

Pending Publication Date: 2021-12-17
SHANDONG LANGCHAO YUNTOU INFORMATION TECH CO LTD
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Problems solved by technology

[0014] The present invention provides a multi-objective optimization method based on similarity measurement in order to solve the problems of slow algorithm convergence and low precision in the multi-objective optimization problem

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  • Multi-objective optimization method based on similarity measurement
  • Multi-objective optimization method based on similarity measurement
  • Multi-objective optimization method based on similarity measurement

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Embodiment 1

[0041] combined with figure 1 , this embodiment proposes a multi-objective optimization method based on similarity measurement. For a multi-objective optimization problem A, the method first uses the Chebyshev decomposition strategy to decompose the multi-objective optimization problem A into N scalar sub-problems, each Each sub-problem contains five elements including weight vector, objective function value, neighborhood, reference point and corresponding solution set; then, each element in the sub-problem is continuously updated to optimize the corresponding objective function, and the final Pareto solution set is obtained. Similarity analysis is performed on the combinations of solutions in the set, and the subset of solutions with the lowest similarity is selected.

[0042] In this embodiment, the Chebyshev decomposition strategy is used to decompose the multi-objective optimization problem into N scalar subproblems, and the specific operations include:

[0043] Step S1.1...

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Abstract

The invention discloses a multi-objective optimization method based on similarity measurement, and relates to the technical field of data processing. The method comprises the following steps: aiming at a multi-objective optimization problem, decomposing the multi-objective optimization problem into N standard sub-problems by adopting a Chebyshev decomposition strategy, wherein each sub-problem comprises five elements including a weight vector, an objective function value, a neighborhood, a reference point and a corresponding solution set; then, updating elements in the sub-problems continuously to optimize corresponding objective functions, obtaining a final Pareto solution set, performing KL divergence calculation on combinations of solutions in the Pareto solution set, and selecting the combination corresponding to the maximum KL value as a final solution subset. According to the invention, the problems of low algorithm convergence speed and low precision in a multi-objective optimization problem can be solved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a multi-objective optimization method based on similarity measurement. Background technique [0002] An optimization problem with only one objective function is a single-objective optimization problem, and an optimization problem with two or more objective functions that needs to be processed simultaneously is called a multi-objective optimization problem. In a multi-objective optimization problem, a solution may be excellent for one objective, but extremely poor for another objective. At this time, a set of compromise solutions is needed. This set of compromise The solution set of is called Pareto optimal solution set or non-dominated solution set. [0003] Many optimization problems in real life are single-objective optimization problems. In such problems, only one of the target indicators is often considered, and the other influencing factor is regarded as a constrain...

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Application Information

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IPC IPC(8): G06F30/20G06F111/04
CPCG06F30/20G06F2111/04
Inventor 张睿智翟盛龙率为朋甘延朋武铁军
Owner SHANDONG LANGCHAO YUNTOU INFORMATION TECH CO LTD
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