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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
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Devido à sua simplicidade e eficiência, a evolução diferencial (DE) tem despertado o interesse de pesquisadores de diversas áreas para a resolução de problemas de otimização global. No entanto, é propenso a convergência prematura em mínimos locais. Para superar esta desvantagem, é proposto um novo algoritmo libélula híbrido com evolução diferencial (Hybrid DA-DE) para resolver problemas de otimização global. Primeiramente, um novo operador de mutação é introduzido com base no algoritmo dragonfly (DA). Em segundo lugar, o fator de escala (F) é ajustado de forma autoadaptável e dependente do indivíduo, sem parâmetros extras. O algoritmo proposto combina a capacidade de exploração do DE e a capacidade de exploração do DA para alcançar soluções globais ideais. A eficácia deste algoritmo é avaliada usando 30 funções clássicas de benchmark com dezesseis algoritmos meta-heurísticos de última geração. Uma série de resultados experimentais mostram que o Hybrid DA-DE supera significativamente outros algoritmos. Enquanto isso, o Hybrid DA-DE tem a melhor adaptabilidade a problemas de alta dimensão.
MeiJun DUAN
National Key Lab. of Fundamental Science on Synthetic Vision
HongYu YANG
National Key Lab. of Fundamental Science on Synthetic Vision
Bo YANG
Sichuan University
XiPing WU
National Key Lab. of Fundamental Science on Synthetic Vision
HaiJun LIANG
Civil Aviation Flight University of China
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MeiJun DUAN, HongYu YANG, Bo YANG, XiPing WU, HaiJun LIANG, "Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 10, pp. 1891-1901, October 2019, doi: 10.1587/transinf.2018EDP7401.
Abstract: Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7401/_p
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@ARTICLE{e102-d_10_1891,
author={MeiJun DUAN, HongYu YANG, Bo YANG, XiPing WU, HaiJun LIANG, },
journal={IEICE TRANSACTIONS on Information},
title={Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization},
year={2019},
volume={E102-D},
number={10},
pages={1891-1901},
abstract={Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.},
keywords={},
doi={10.1587/transinf.2018EDP7401},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization
T2 - IEICE TRANSACTIONS on Information
SP - 1891
EP - 1901
AU - MeiJun DUAN
AU - HongYu YANG
AU - Bo YANG
AU - XiPing WU
AU - HaiJun LIANG
PY - 2019
DO - 10.1587/transinf.2018EDP7401
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2019
AB - Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.
ER -