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On the Selection of Decomposition Methods for Large Scale Fully Non-separable Problems

Authors:

Saman Kumara HalgamugeAuthors Info & Claims

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1213 - 1216

https://doi.org/10.1145/2739482.2768483

Published: 11 July 2015 Publication History

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Abstract

Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However, solution quality relies heavily on the decomposition method used. In recent years, a number of decomposition methods have been proposed, which raises another research question: Which decomposition method is best for a given large scale optimization problem? In this paper, we focus on the selection of the best decomposition method for large scale fully non-separable problems. Four decomposition methods are compared on a suite of benchmark functions. We observe that the random grouping method obtains the best solution quality on the benchmark large scale fully non-separable problems.

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Cited By

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S. Latha RSaravana Balaji BBacanin NStrumberger IZivkovic MKabiljo M(2022)Feature Selection Using Grey Wolf Optimization with Random Differential GroupingComputer Systems Science and Engineering10.32604/csse.2022.02048743:1(317-332)Online publication date: 2022
https://doi.org/10.32604/csse.2022.020487
Rashid AAhmed MSikos LHaskell-Dowland P(2020)Cooperative co-evolution for feature selection in Big Data with random feature groupingJournal of Big Data10.1186/s40537-020-00381-y7:1Online publication date: 4-Dec-2020
https://doi.org/10.1186/s40537-020-00381-y
Omidvar MYang MMei YLi XYao X(2017)DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2017.269422121:6(929-942)Online publication date: Dec-2017
https://doi.org/10.1109/TEVC.2017.2694221

Index Terms

On the Selection of Decomposition Methods for Large Scale Fully Non-separable Problems
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1568 pages

ISBN:9781450334884

DOI:10.1145/2739482

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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S. Latha RSaravana Balaji BBacanin NStrumberger IZivkovic MKabiljo M(2022)Feature Selection Using Grey Wolf Optimization with Random Differential GroupingComputer Systems Science and Engineering10.32604/csse.2022.02048743:1(317-332)Online publication date: 2022
https://doi.org/10.32604/csse.2022.020487
Rashid AAhmed MSikos LHaskell-Dowland P(2020)Cooperative co-evolution for feature selection in Big Data with random feature groupingJournal of Big Data10.1186/s40537-020-00381-y7:1Online publication date: 4-Dec-2020
https://doi.org/10.1186/s40537-020-00381-y
Omidvar MYang MMei YLi XYao X(2017)DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2017.269422121:6(929-942)Online publication date: Dec-2017
https://doi.org/10.1109/TEVC.2017.2694221

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