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A novel memetic algorithm based on invasive weed optimization and differential evolution for constrained optimization

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Abstract

This paper presents a novel memetic algorithm, named as IWO_DE, to tackle constrained numerical and engineering optimization problems. In the proposed method, invasive weed optimization (IWO), which possesses the characteristics of adaptation required in memetic algorithm, is firstly considered as a local refinement procedure to adaptively exploit local regions around solutions with high fitness. On the other hand, differential evolution (DE) is introduced as the global search model to explore more promising global area. To accommodate the hybrid method with the task of constrained optimization, an adaptive weighted sum fitness assignment and polynomial distribution are adopted for the reproduction and the local dispersal process of IWO, respectively. The efficiency and effectiveness of the proposed approach are tested on 13 well-known benchmark test functions. Besides, our proposed IWO_DE is applied to four well-known engineering optimization problems. Experimental results suggest that IWO_DE can successfully achieve optimal results and is very competitive compared with other state-of-art algorithms.

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Acknowledgments

The research reported herein was supported in part by the Fundamental Research Funds for the Central Universities of China (NO. NS2012074), Research Fund for the Doctoral Program of Higher Education of China (NO. 20123218120041) and National Natural Science Foundation of China (No. 61175073).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu, China

    Xinye Cai & Zhenzhou Hu

  2. Department of Mechatronic Engineering, School of Engineering, Shantou University, Guangdong, China

    Zhun Fan

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  1. Xinye Cai
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  2. Zhenzhou Hu
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  3. Zhun Fan
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Correspondence to Xinye Cai.

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Communicated by Y.-S. Ong.

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Cai, X., Hu, Z. & Fan, Z. A novel memetic algorithm based on invasive weed optimization and differential evolution for constrained optimization. Soft Comput 17, 1893–1910 (2013). https://doi.org/10.1007/s00500-013-1028-4

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