Differential Evolution for High-dimensional Function Optimization

Z Yang; K Tang; Xin Yao

doi:10.1109/CEC.2007.4424929

Differential Evolution for High-dimensional Function Optimization

Z Yang, K Tang, Xin Yao

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

163 Citations (Scopus)

Abstract

Most reported studies on differential evolution (DE) are obtained using low-dimensional problems, e.g., smaller than 100, which are relatively small for many real-world problems. In this paper we propose two new efficient DE variants, named DECC-I and DECC-II, for high-dimensional optimization (up to 1000 dimensions). The two algorithms are based on a cooperative coevolution framework incorporated with several novel strategies. The new strategies are mainly focus on problem decomposition and subcomponents cooperation. Experimental results have shown that these algorithms have superior performance on a set of widely used benchmark functions.

Original language	English
Title of host publication	IEEE Congress on Evolutionary Computation, 2007. CEC 2007.
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3523-3530
Number of pages	8
ISBN (Electronic)	978-1-4244-1340-9
ISBN (Print)	978-1-4244-1339-3
DOIs	https://doi.org/10.1109/CEC.2007.4424929
Publication status	Published - 1 Sept 2007
Event	IEEE Congress on Evolutionary Computation, 2007 (CEC 2007) - Singapore, Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Conference

Conference	IEEE Congress on Evolutionary Computation, 2007 (CEC 2007)
Country/Territory	Singapore
City	Singapore
Period	25/09/07 → 28/09/07

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10.1109/CEC.2007.4424929

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title = "Differential Evolution for High-dimensional Function Optimization",

abstract = "Most reported studies on differential evolution (DE) are obtained using low-dimensional problems, e.g., smaller than 100, which are relatively small for many real-world problems. In this paper we propose two new efficient DE variants, named DECC-I and DECC-II, for high-dimensional optimization (up to 1000 dimensions). The two algorithms are based on a cooperative coevolution framework incorporated with several novel strategies. The new strategies are mainly focus on problem decomposition and subcomponents cooperation. Experimental results have shown that these algorithms have superior performance on a set of widely used benchmark functions.",

author = "Z Yang and K Tang and Xin Yao",

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doi = "10.1109/CEC.2007.4424929",

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pages = "3523--3530",

booktitle = "IEEE Congress on Evolutionary Computation, 2007. CEC 2007.",

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note = "IEEE Congress on Evolutionary Computation, 2007 (CEC 2007) ; Conference date: 25-09-2007 Through 28-09-2007",

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T1 - Differential Evolution for High-dimensional Function Optimization

AU - Yang, Z

AU - Tang, K

AU - Yao, Xin

PY - 2007/9/1

Y1 - 2007/9/1

N2 - Most reported studies on differential evolution (DE) are obtained using low-dimensional problems, e.g., smaller than 100, which are relatively small for many real-world problems. In this paper we propose two new efficient DE variants, named DECC-I and DECC-II, for high-dimensional optimization (up to 1000 dimensions). The two algorithms are based on a cooperative coevolution framework incorporated with several novel strategies. The new strategies are mainly focus on problem decomposition and subcomponents cooperation. Experimental results have shown that these algorithms have superior performance on a set of widely used benchmark functions.

AB - Most reported studies on differential evolution (DE) are obtained using low-dimensional problems, e.g., smaller than 100, which are relatively small for many real-world problems. In this paper we propose two new efficient DE variants, named DECC-I and DECC-II, for high-dimensional optimization (up to 1000 dimensions). The two algorithms are based on a cooperative coevolution framework incorporated with several novel strategies. The new strategies are mainly focus on problem decomposition and subcomponents cooperation. Experimental results have shown that these algorithms have superior performance on a set of widely used benchmark functions.

U2 - 10.1109/CEC.2007.4424929

DO - 10.1109/CEC.2007.4424929

M3 - Conference contribution

SN - 978-1-4244-1339-3

SP - 3523

EP - 3530

BT - IEEE Congress on Evolutionary Computation, 2007. CEC 2007.

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - IEEE Congress on Evolutionary Computation, 2007 (CEC 2007)

Y2 - 25 September 2007 through 28 September 2007

ER -

Differential Evolution for High-dimensional Function Optimization

Abstract

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