Multi-start jade with knowledge Transfer for numerical optimization

Fei PENG; Ke TANG; Guoliang CHEN; Xin YAO

doi:10.1109/CEC.2009.4983171

Multi-start jade with knowledge Transfer for numerical optimization

Fei PENG, Ke TANG, Guoliang CHEN, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

73 Citations (Scopus)

Abstract

JADE is a recent variant of Differential Evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we observed that the reliability, which is an important characteristic of stochastic algorithms, of JADE still needs to be improved. In this paper we apply two strategies together on the original JADE, to dedicatedly improve the reliability of it. We denote the new algorithm as rJADE. In rJADE, we first modify the control parameter adaptation strategy of JADE by adding a weighting strategy. Then, a "restart with knowledge transfer" strategy is applied by utilizing the knowledge obtained from previous failures to guide the subsequent search. Experimental studies show that the proposed rJADE achieved significant improvements on a set of widely used benchmark functions. © 2009 IEEE.

Original language	English
Title of host publication	2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages	1889-1895
Number of pages	7
DOIs	https://doi.org/10.1109/CEC.2009.4983171
Publication status	Published - May 2009
Externally published	Yes

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title = "Multi-start jade with knowledge Transfer for numerical optimization",

abstract = "JADE is a recent variant of Differential Evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we observed that the reliability, which is an important characteristic of stochastic algorithms, of JADE still needs to be improved. In this paper we apply two strategies together on the original JADE, to dedicatedly improve the reliability of it. We denote the new algorithm as rJADE. In rJADE, we first modify the control parameter adaptation strategy of JADE by adding a weighting strategy. Then, a {"}restart with knowledge transfer{"} strategy is applied by utilizing the knowledge obtained from previous failures to guide the subsequent search. Experimental studies show that the proposed rJADE achieved significant improvements on a set of widely used benchmark functions. {\textcopyright} 2009 IEEE.",

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year = "2009",

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doi = "10.1109/CEC.2009.4983171",

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AU - TANG, Ke

AU - CHEN, Guoliang

AU - YAO, Xin

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N2 - JADE is a recent variant of Differential Evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we observed that the reliability, which is an important characteristic of stochastic algorithms, of JADE still needs to be improved. In this paper we apply two strategies together on the original JADE, to dedicatedly improve the reliability of it. We denote the new algorithm as rJADE. In rJADE, we first modify the control parameter adaptation strategy of JADE by adding a weighting strategy. Then, a "restart with knowledge transfer" strategy is applied by utilizing the knowledge obtained from previous failures to guide the subsequent search. Experimental studies show that the proposed rJADE achieved significant improvements on a set of widely used benchmark functions. © 2009 IEEE.

AB - JADE is a recent variant of Differential Evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we observed that the reliability, which is an important characteristic of stochastic algorithms, of JADE still needs to be improved. In this paper we apply two strategies together on the original JADE, to dedicatedly improve the reliability of it. We denote the new algorithm as rJADE. In rJADE, we first modify the control parameter adaptation strategy of JADE by adding a weighting strategy. Then, a "restart with knowledge transfer" strategy is applied by utilizing the knowledge obtained from previous failures to guide the subsequent search. Experimental studies show that the proposed rJADE achieved significant improvements on a set of widely used benchmark functions. © 2009 IEEE.

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U2 - 10.1109/CEC.2009.4983171

DO - 10.1109/CEC.2009.4983171

M3 - Conference paper (refereed)

SN - 9781424429592

SP - 1889

EP - 1895

BT - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

ER -

Multi-start jade with knowledge Transfer for numerical optimization

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