Summary
|
For Full-Text PDF, please login, if you are a member of IEICE, or go to Pay Per View on menu list, if you are a nonmember of IEICE. |
|
Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization MeiJun DUAN HongYu YANG Bo YANG XiPing WU HaiJun LIANG Publication 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 Keyword: global optimization, differential evolution, dragonfly algorithm, hybrid DA-DE, self-adaptive and individual-dependent, Full Text: FreePDF(2.5MB) Summary: 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. | ||||||||||
| ||||||||||
