Low-dimensional space modeling-based differential evolution for large scale global optimization problems
| dc.contributor.author | Fonseca, Thiago Henrique Lemos | |
| dc.contributor.author | Nassar, Silvia Modesto | |
| dc.contributor.author | de Oliveira, Alexandre César Muniz | |
| dc.contributor.author | Agard, Bruno | |
| dc.contributor.funder | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior | en |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.contributor.funder | European Regional Development Fund | en |
| dc.date.accessioned | 2023-01-26T13:14:29Z | |
| dc.date.available | 2023-01-26T13:14:29Z | |
| dc.date.issued | 2022-12-07 | |
| dc.date.updated | 2023-01-26T12:04:57Z | |
| dc.description.abstract | Large-Scale Global Optimization (LSGO) has been an active research field. Part of this interest is supported by its application to cutting-edge research such as Deep Learning, Big Data, and complex real-world problems such as image encryption, real-time traffic management, and more. However, the high dimensionality makes solving LSGO a significant challenge. Some recent research deal with the high dimensionality by mapping the optimization process to a reduced alternative space. Nonetheless, these works suffer from the changes in the search space topology and the loss of information caused by the dimensionality reduction. This paper proposes a hybrid metaheuristic, so-called LSMDE (Low-dimensional Space Modeling-based Differential Evolution), that uses the Singular Value Decomposition to build a low-dimensional search space from the features of candidate solutions generated by a new SHADE-based algorithm (GM-SHADE). GM-SHADE combines a Gaussian Mixture Model (GMM) and two specialized local algorithms: MTS-LS1 and L-BFGS-B, to promote a better exploration of the reduced search space. GMM mitigates the loss of information in mapping high-dimensional individuals to low-dimensional individuals. Furthermore, the proposal does not require prior knowledge of the search space topology, which makes it more flexible and adaptable to different LSGO problems. The results indicate that LSMDE is the most efficient method to deal with partially separable functions compared to other state-of-the-art algorithms and has the best overall performance in two of the three proposed experiments. Experimental results also show that the new approach achieves competitive results for non-separable and overlapping functions on the most recent test suite for LSGO problems. | en |
| dc.description.sponsorship | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001); Science Foundation Ireland (grant no.SFI/16/RC/3918 CONFIRM; Marie Sklodowska-Curie grant agreement no. 847.577) | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Fonseca, T. H. L., Nassar, S. M., de Oliveira, A. C. M. and Agard, B. (2022) 'Low-dimensional space modeling-based differential evolution for large scale global optimization problems', IEEE Transactions on Evolutionary Computation. doi: 10.1109/TEVC.2022.3227440 | en |
| dc.identifier.doi | 10.1109/TEVC.2022.3227440 | en |
| dc.identifier.eissn | 1941-0026 | |
| dc.identifier.issn | 1089-778X | |
| dc.identifier.journaltitle | IEEE Transactions on Evolutionary Computation | en |
| dc.identifier.uri | https://hdl.handle.net/10468/14138 | |
| dc.language.iso | en | en |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
| dc.relation.project | info:eu-repo/grantAgreement/EC/H2020::MSCA-COFUND-FP/847577/EU/Smart Manufacturing Advanced Research Training for Industry 4.0/SMART 4.0 | |
| dc.rights | © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
| dc.subject | Clustering algorithms | en |
| dc.subject | Differential evolution | en |
| dc.subject | Dimensionality reduction | en |
| dc.subject | Gaussian mixture model | en |
| dc.subject | Heuristic algorithms | en |
| dc.subject | Optimization | en |
| dc.subject | Search problems | en |
| dc.subject | Singular value decomposition | en |
| dc.subject | Sociology | en |
| dc.subject | Statistics | en |
| dc.title | Low-dimensional space modeling-based differential evolution for large scale global optimization problems | en |
| dc.type | Article (peer-reviewed) | en |
