| 引用本文: | 张艺瀛,金志刚.求解多峰优化问题的改进布谷鸟算法[J].哈尔滨工业大学学报,2019,51(11):89.DOI:10.11918/j.issn.0367-6234.201902003 |
| ZHANG Yiying,JIN Zhigang.An improved cuckoo search for multimodal optimization problems[J].Journal of Harbin Institute of Technology,2019,51(11):89.DOI:10.11918/j.issn.0367-6234.201902003 |
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| 摘要: |
| 布谷鸟算法是一种简便而高效的元启发式算法.然而,布谷鸟算法在求解复杂的多峰优化问题时通常存在易陷入局部最优解的缺点.针对布谷鸟算法的这种缺点,结合神经网络算法和布谷鸟算法的特性,提出一种基于神经网络的布谷鸟算法.该算法的核心思想是借助改进神经网络算法的强大全局搜索能力和动态种群策略来平衡布谷鸟算法的全局搜索能力和局部搜索能力,从而减少布谷鸟算法陷入局部最优的可能性.该算法首先将种群中的个体依照适应度值的优劣进行排序,然后对种群中最好的一半个体通过布谷鸟算法进行优化,对种群中最差的一半个体通过改进的神经网络算法进行优化,最后将所有个体组成一个新的种群,并从中筛选出最优解. 采用24个复杂基准测试函数检验所提出算法求解多峰优化问题的性能,并将优化结果与神经网络算法,布谷鸟算法以及一些改进的布谷鸟算法所获取的优化结果相比较.实验结果表明:所提出的算法充分地展现了神经网络算法和布谷鸟算法的优势,其在求解质量,求解效率以及求解稳定性上均显著优于其它算法. |
| 关键词: 人工神经网络 生物神经系统 布谷鸟搜索 多峰优化 |
| DOI：10.11918/j.issn.0367-6234.201902003 |
| 分类号:TP18 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(71502125) |
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| An improved cuckoo search for multimodal optimization problems |
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ZHANG Yiying,JIN Zhigang
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(School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)
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| Abstract: |
| Cuckoo search algorithm is a simple and efficient meta-heuristic algorithm, while it can be easily trapped into local optimum when solving complex multimodal optimization problems. To tackle this problem, an improved cuckoo search algorithm based on neural networks was proposed by combining the characteristics of neural network algorithm and cuckoo search algorithm. The core idea of this algorithm is to balance global search ability and local search ability of cuckoo search algorithm with powerful global search ability of the improved neural network algorithm and dynamic population strategy, thereby reducing the possibility of the cuckoo algorithm falling into local optimum. The algorithm firstly sorts the individuals in the population according to the fitness values. Then the best half individuals of the population are performed by the cuckoo search algorithm, whereas the worst half individuals are optimized by the improved neural network algorithm. Finally all individuals are grouped into a new population, from which the optimal solution can be selected. In this experiment, 24 complex multimodal optimization problems were employed to study the optimization performance and compare between the proposed algorithm and neural network algorithm, cuckoo search algorithm, and other improved cuckoo algorithms. Results show that the proposed algorithm fully demonstrated the advantages of the modified neural network algorithm and the cuckoo search algorithm, which was significantly better than other algorithms in resolution quality, convergence speed, and stability. |
| Key words: artificial neural networks biological nervous cuckoo search multimodal optimization |
