A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

Determining the projection direction vector (PDV) is essential to the projection pursuit evaluation method for high-dimensional problems under multiple uncertainties. Although the PP method using a cloud model can facilitate interpretation of the fuzziness and randomness of the PDV, it ignores the a...

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Autores principales:	Mingwu Wang, Yan Wang, Fengqiang Shen, Juliang Jin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	projection pursuit fruit fly optimization algorithm metaheuristics connection cloud model set pair Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/94e3e76d2d8b4a3e82f8dbe176ad9aa6
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id	oai:doaj.org-article:94e3e76d2d8b4a3e82f8dbe176ad9aa6
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spelling	oai:doaj.org-article:94e3e76d2d8b4a3e82f8dbe176ad9aa62021-11-25T19:06:04ZA Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability10.3390/sym131120162073-8994https://doaj.org/article/94e3e76d2d8b4a3e82f8dbe176ad9aa62021-10-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2016https://doaj.org/toc/2073-8994Determining the projection direction vector (PDV) is essential to the projection pursuit evaluation method for high-dimensional problems under multiple uncertainties. Although the PP method using a cloud model can facilitate interpretation of the fuzziness and randomness of the PDV, it ignores the asymmetry of the PDV and the fact that indicators are actually distributed over finite intervals; it quickly falls into premature defects. Therefore, a novel PP evaluation method based on the connection cloud model (CCM) is discussed to remedy these drawbacks. In this approach, adaptive numerical characteristics of the CCM are adopted to represent the randomness and fuzziness of the candidate PDV and evaluation indicators. Meanwhile, to avoid complex computing and to accelerate the convergence speed of the optimization procedure, an improved fruit fly optimization algorithm (FOA) is set up to find the rational PDV. Alternatively, candidate PDVs are mutated based on the mechanism “pick the best of the best” using set pair analysis (SPA) and chaos theory. Furthermore, the applicability and reliability are discussed based on an illustrative example of slope stability evaluation and comparisons with the neural network method and the PP evaluation method based on the other FOAs and the genetic algorithm. Results indicate that the proposed method with simpler code and quicker convergence speed has good global ergodicity and local searching capabilities, and can better explore the structure of high-dimensional data with multiple uncertainties and asymmetry of the PDV relative to other methods.Mingwu WangYan WangFengqiang ShenJuliang JinMDPI AGarticleprojection pursuitfruit fly optimization algorithmmetaheuristicsconnection cloud modelset pairMathematicsQA1-939ENSymmetry, Vol 13, Iss 2016, p 2016 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	projection pursuit fruit fly optimization algorithm metaheuristics connection cloud model set pair Mathematics QA1-939
spellingShingle	projection pursuit fruit fly optimization algorithm metaheuristics connection cloud model set pair Mathematics QA1-939 Mingwu Wang Yan Wang Fengqiang Shen Juliang Jin A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability
description	Determining the projection direction vector (PDV) is essential to the projection pursuit evaluation method for high-dimensional problems under multiple uncertainties. Although the PP method using a cloud model can facilitate interpretation of the fuzziness and randomness of the PDV, it ignores the asymmetry of the PDV and the fact that indicators are actually distributed over finite intervals; it quickly falls into premature defects. Therefore, a novel PP evaluation method based on the connection cloud model (CCM) is discussed to remedy these drawbacks. In this approach, adaptive numerical characteristics of the CCM are adopted to represent the randomness and fuzziness of the candidate PDV and evaluation indicators. Meanwhile, to avoid complex computing and to accelerate the convergence speed of the optimization procedure, an improved fruit fly optimization algorithm (FOA) is set up to find the rational PDV. Alternatively, candidate PDVs are mutated based on the mechanism “pick the best of the best” using set pair analysis (SPA) and chaos theory. Furthermore, the applicability and reliability are discussed based on an illustrative example of slope stability evaluation and comparisons with the neural network method and the PP evaluation method based on the other FOAs and the genetic algorithm. Results indicate that the proposed method with simpler code and quicker convergence speed has good global ergodicity and local searching capabilities, and can better explore the structure of high-dimensional data with multiple uncertainties and asymmetry of the PDV relative to other methods.
format	article
author	Mingwu Wang Yan Wang Fengqiang Shen Juliang Jin
author_facet	Mingwu Wang Yan Wang Fengqiang Shen Juliang Jin
author_sort	Mingwu Wang
title	A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability
title_short	A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability
title_full	A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability
title_fullStr	A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability
title_full_unstemmed	A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability
title_sort	heuristic projection pursuit method based on a connection cloud model and set pair analysis for evaluation of slope stability
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/94e3e76d2d8b4a3e82f8dbe176ad9aa6
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_version_	1718410292090634240

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

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