A Decomposition-based Approach for Constrained Large-Scale Global Optimization

Evgenii Sopov, Alexey Vakhnin

2019

Abstract

Many real-world global optimization problems are too complex for comprehensive analysis and are viewed as “black-box” (BB) optimization problems. Modern BB optimization has to deal with growing dimensionality. Large-scale global optimization (LSGO) is known as a hard problem for many optimization techniques. Nevertheless, many efficient approaches have been proposed for solving LSGO problems. At the same time, LSGO does not take into account such features of real-world optimization problems as constraints. The majority of state-of-the-art techniques for LSGO are based on problem decomposition and use evolutionary algorithms as the core optimizer. In this study, we have investigated the performance of a novel decomposition-based approach for constrained LSGO (cLSGO), which combines cooperative coevolution of SHADE algorithms with the ε-constraint handling technique for differential evolution. We have introduced some benchmark problems for cLSGO, based on scalable separable and non-separable problems from IEEE CEC 2017 benchmark for constrained real parameter optimization. We have tested SHADE with the penalty approach, regular ε-SHADE and ε-SHADE with problem decomposition. The results of numerical experiments are presented and discussed.

Paper Citation

in Harvard Style

Sopov E. and Vakhnin A. (2019). A Decomposition-based Approach for Constrained Large-Scale Global Optimization. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA; ISBN 978-989-758-384-1, SciTePress, pages 147-154. DOI: 10.5220/0007966901470154

in Bibtex Style

@conference{ecta19,
author={Evgenii Sopov and Alexey Vakhnin},
title={A Decomposition-based Approach for Constrained Large-Scale Global Optimization},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA},
year={2019},
pages={147-154},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007966901470154},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: ECTA
TI - A Decomposition-based Approach for Constrained Large-Scale Global Optimization
SN - 978-989-758-384-1
AU - Sopov E.
AU - Vakhnin A.
PY - 2019
SP - 147
EP - 154
DO - 10.5220/0007966901470154
PB - SciTePress