Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

MeiJun DUAN  HongYu YANG  Bo YANG  XiPing WU  HaiJun LIANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.10   pp.1891-1901
Publication Date: 2019/10/01
Publicized: 2019/07/17
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7401
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
global optimization,  differential evolution,  dragonfly algorithm,  hybrid DA-DE,  self-adaptive and individual-dependent,  

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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.