Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

TL;DR: IAFOA is used to solve three engineering optimization problems for the purpose of verifying its practicability, and experiment results show that IAFOA can generate the best solutions compared with other ten algorithms.

TL;DR: An enhanced fruit fly optimizer, termed SCA_FOA, is developed by introducing the logic of the sine cosine algorithm (SCA) in the process of searching for food utilizing the osphresis organ, and it demonstrates that it is superior to other competitive algorithms.

TL;DR: In this article, surface distresses of pavement are used to estimate the remaining service life (RSL) of pavement, which is a challenging task for road maintenance and transportation engineering, and the conventional approach to predict RSL involves using non-destructive tests, in addition to being costly, interfere with traffic flow and compromise operational safety.

TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.

TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.

TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.

TL;DR: This work describes a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size, and compares generalization performance of SVM regression under sparse sample settings with regression using 'least-modulus' loss (epsilon=0) and standard squared loss.