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Journal of Applied Geodesy

Editor-in-Chief: Kahmen, Heribert / Rizos, Chris


CiteScore 2018: 1.61

SCImago Journal Rank (SJR) 2018: 0.532
Source Normalized Impact per Paper (SNIP) 2018: 1.064

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1862-9024
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Solution method for ill-conditioned problems based on a new improved fruit fly optimization algorithm

Qian Fan / Xiaolin MengORCID iD: https://orcid.org/0000-0003-2440-8054 / Chengquan Xu / Jiayong Yu
Published Online: 2019-11-08 | DOI: https://doi.org/10.1515/jag-2019-0025

Abstract

Based on deeply analysis for optimization process of basic fruit fly optimization algorithm (FOA), a new improved FOA (IFOA) method is proposed, which modifies random search direction, increases the adjustment coefficient of search radius, and establishes a multi-sub-population solution mechanism. The proposed method can process the nonlinear objective function that has non-zero and non-negative extreme points. In the paper, IFOA method is applied to ill-conditioned problem solution in the field of surveying data processing. Application of the proposed method on two practical examples show that solution accuracy of IFOA is superior to that of three well-known intelligent optimization algorithms and two existing improved FOA methods, and it is also better than truncated singular value decomposition method and ridge estimation method. In addition, compared with intelligent search method represented by particle swarm optimization algorithm, The IFOA method has the advantages of less parameter settings, simple optimization process and easy program implementation. So, IFOA method is feasible, effective and practical in solving ill-conditioned problems.

Keywords: ill-conditioned problem solution; improved fruit fly optimization algorithm; random search direction; multi-sub-population; particle swarm optimization

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About the article

Received: 2019-06-03

Accepted: 2019-10-23

Published Online: 2019-11-08


This paper is supported by National Natural Science Foundation of China (No. 41404008), The Science and Technology Program of Fuzhou (No. 2017-G-73), Open Foundation of Key Laboratory for Digital Land and Resources of Jiangxi Province (No. DLLJ201911), Guiding Project of Fujian Science and Technology Program (No. 2018Y0021).


Citation Information: Journal of Applied Geodesy, ISSN (Online) 1862-9024, ISSN (Print) 1862-9016, DOI: https://doi.org/10.1515/jag-2019-0025.

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