Multi-mode characteristic verification universal method based on dynamic time bending algorithm

Abstract

The invention discloses a multi-mode characteristic verification universal method based on a dynamic time bending algorithm, and belongs to the field of multi-mode optimization. The method takes a dynamic time bending algorithm as a theoretical support; the method specifically comprises the following steps: for a single-target problem, encoding and modeling an actual problem, then carrying out random walk sampling, calculating a dynamic time bending distance by using a sampling curve and a standard multimode test function, analyzing the similarity between the curves, and further judging the multimode characteristic of the actual problem; for a multi-target problem, after an actual problem is abstracted into a function curve, uniformly grouping a curve decision space, adopting a multi-target optimization algorithm for optimization to obtain Pareto leading edges corresponding to grouping areas, and calculating the dynamic time bending distance between multiple groups of leading edges; and analyzing the similarity between Pareto front edges of different areas, and judging the multi-mode characteristic of an actual problem in a decision space. The universal multi-mode characteristic verification method is high in universality, clear and rigorous in verification logic and good in reasonability.

Description

technical field [0001] The invention belongs to the field of multi-mode optimization, and in particular relates to a general method for multi-mode pattern verification based on a dynamic time warping algorithm. Background technique [0002] In recent years, with the rapid development of science and technology, the degree of automation in the industrial production process has been continuously improved, and a series of practical production problems such as test task scheduling have been derived, and better decision-making solutions are urgently needed. Due to the influence of the actual production environment and various resource conditions, this type of problem often has multiple feasible optimal solutions. In order to achieve smooth operation in the production process and better deal with various uncertain factors that may occur during automated production, the producer often hopes to know the global optimal solution and a series of high-quality local extremum in the existi...

AI Technical Summary

Problems solved by technology

Ordinary Euclidean distance calculations cannot recognize deformations such as time series expansion and contraction, and are not suitable for two time series with different lengths; however, the application of dynamic time warping algorithm can process the expansion and contraction changes of time series along the time axis, and can find two The points that should be aligned in the sequence, and then calculate the distance between these points and accumulate them, is the real distance between the two time series

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