ORCID Profile
0000-0003-1944-4624
Current Organisation
University of Leeds
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Publisher: ACM
Date: 02-07-2018
Publisher: ACM
Date: 02-07-2018
Publisher: Springer International Publishing
Date: 2015
Publisher: ACM
Date: 15-07-2017
Publisher: IEEE
Date: 05-2015
Publisher: Elsevier BV
Date: 04-2019
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 07-2010
Publisher: Elsevier BV
Date: 11-2018
Publisher: ACM
Date: 15-07-2023
Publisher: Elsevier BV
Date: 09-2015
Publisher: IEEE
Date: 07-2014
Publisher: Elsevier BV
Date: 06-2019
Publisher: IEEE
Date: 07-2010
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 06-2017
Publisher: Association for Computing Machinery (ACM)
Date: 03-06-2016
DOI: 10.1145/2791291
Abstract: This article proposes a competitive ide-and-conquer algorithm for solving large-scale black-box optimization problems for which there are thousands of decision variables and the algebraic models of the problems are unavailable. We focus on problems that are partially additively separable, since this type of problem can be further decomposed into a number of smaller independent subproblems. The proposed algorithm addresses two important issues in solving large-scale black-box optimization: (1) the identification of the independent subproblems without explicitly knowing the formula of the objective function and (2) the optimization of the identified black-box subproblems. First, a Global Differential Grouping (GDG) method is proposed to identify the independent subproblems. Then, a variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is adopted to solve the subproblems resulting from its rotation invariance property. GDG and CMA-ES work together under the cooperative co-evolution framework. The resultant algorithm, named CC-GDG-CMAES, is then evaluated on the CEC’2010 large-scale global optimization (LSGO) benchmark functions, which have a thousand decision variables and black-box objective functions. The experimental results show that, on most test functions evaluated in this study, GDG manages to obtain an ideal partition of the index set of the decision variables, and CC-GDG-CMAES outperforms the state-of-the-art results. Moreover, the competitive performance of the well-known CMA-ES is extended from low-dimensional to high-dimensional black-box problems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: ACM
Date: 12-07-2011
Publisher: Elsevier BV
Date: 03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Mohammad Nabi Omidvar.