基于小波精英解学习和多角度搜索的新型阴阳平衡优化算法
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上海理工大学

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TP 301.6

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A novel Yin-Yang pair optimization algorithm based on the Wavelet elite solutions learning and multi-angle search
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University of Shanghai for Science and Technology

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    摘要:

    针对基本阴阳平衡优化算法计算精度低和优化速度慢等问题,提出一种新型阴阳平衡优化算法.首先,设计小波精英解学习策略,充分利用精英解的进化信息来产生高质量的解,用于算法的全局勘探和局部开发;然后,将搜索角度引入到解更新方程中,以实现对算法搜索空间的全方位搜索.对新算法的收敛性进行了理论分析.采用连续优化测试函数和瓶颈旅行商问题进行数值实验,并将新算法和多种智能优化方法进行比较.实验结果表明新算法具有更好的优化性能.

    Abstract:

    In order to solve the problem of low calculation accuracy and slow convergence velocity of the Yin-Yang pair optimization (YYPO) algorithm, a novel YYPO (NYYPO) algorithm, based on the Wavelet elite solutions learning and multi-angle search, is presented in this paper. Firstly, the strategy of Wavelet elite solutions learning is proposed to make full use of the evolutionary information of elite solutions. High-quality solutions generated by this strategy are used in the global exploration and local exploitation of algorithm. Secondly, the search angle is introduced into the solution update equation, which can realize the all-round search of algorithm search space. The convergence of the proposed algorithm is analysed. The continuous functions and the bottleneck traveling salesman problem are employed in the numerical experiments. NYYPO is compared with many intelligent optimization algorithms. Experimental results demonstrate that the presented NYYPO algorithm has better optimization performance.

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历史
  • 收稿日期:2021-02-07
  • 最后修改日期:2021-05-26
  • 录用日期:2021-06-03
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