基于改进螺旋更新位置模型的鲸鱼优化算法

吴泽忠, 宋菲

系统工程理论与实践 ›› 2019, Vol. 39 ›› Issue (11) : 2928-2944.

PDF(2030 KB)
中国科学院数学与系统科学研究院期刊网
PDF(2030 KB)
系统工程理论与实践 ›› 2019, Vol. 39 ›› Issue (11) : 2928-2944. DOI: 10.12011/1000-6788-2018-2156-17
论文

基于改进螺旋更新位置模型的鲸鱼优化算法

    吴泽忠, 宋菲
作者信息 +

Whale optimization algorithm based on improved spiral update position model

    WU Zezhong, SONG Fei
Author information +
文章历史 +

摘要

针对鲸鱼算法后期种群的多样性丢失问题，通过螺旋更新位置模型的改进并结合对立学习策略、随机调整参数、正态变异操作等已有方法改进鲸鱼优化算法.采用对立学习策略对鲸鱼种群初始化，为全局搜索奠定基础；利用随机调整控制参数的策略，避免了算法后期陷入局部最优；正态变异算子与改进螺旋更新位置对鲸鱼种群进行干扰，避免种群后期向某个最优区域靠拢，增大算法的全局搜索能力.选取文献[4]中23个国际标准测试函数，包括单峰、多峰以及固定维数函数，对改进的算法进行低维测试；选取文献[12]中的25个单峰和多峰国际标准测试函数，对改进的算法进行高维测试.结果表明，IMWOA算法在收敛精度、收敛速度上均明显优于原WOA算法且具有普遍适用性、稳定性和解决超大规模优化问题的能力.

Abstract

In this paper, the spiral update location model is improved for the loss of population diversity of the whale algorithm in the late. At the same time, the whale optimization algorithm is improved by combining existing methods such as opposite learning strategies, random adjustment parameters, and normal mutation operations. Using the opposite learning strategy to initialize the whale population, lay the foundation for the global search. Using the strategy of randomly adjusting the control parameters, it avoids the late fall into local optimum. Normal mutation operator and improved spiral update position model are designed to interfere with the whale population, avoiding the late arrival of the population to an optimal region and increasing the global search ability of the algorithm. In this paper, the improved algorithm for low-dimensional testing is based on 23 international standard test functions in the literature[4], including unimodal, multimodal, and fixed-dimensional functions. The improved algorithm for high-dimensional testing is based on 25 international standard test functions in the literature[12]. The results show that the IMWOA algorithm is superior to the original WOA algorithm in terms of convergence accuracy and convergence speed, and has universal applicability, stability and ability to solve hyperscale optimization problems.

关键词

鲸鱼优化算法 / 随机调整参数 / 正态变异算子 / 螺旋更新位置模型 / 超大规模优化问题

Key words

whale optimization algorithm / random adjustment parameters / normal mutation operator / spiral update position model / hyper-scale optimization problem

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用
吴泽忠 , 宋菲. 基于改进螺旋更新位置模型的鲸鱼优化算法. 系统工程理论与实践, 2019, 39(11): 2928-2944 https://doi.org/10.12011/1000-6788-2018-2156-17
WU Zezhong , SONG Fei. Whale optimization algorithm based on improved spiral update position model. Systems Engineering - Theory & Practice, 2019, 39(11): 2928-2944 https://doi.org/10.12011/1000-6788-2018-2156-17
中图分类号： TP301.6   

参考文献

[1] Holland J H. Genetic algorithms[J]. Scientific American, 1992(267):66-72.
[2] Kirkpatrick S, Gelatt C D, Vecchi M P, et al. Optimization by simulated annealing[J]. Science, 1983, 220(4598):671-680.
[3] Kennedy J, Eberhart R C. Particle swarm optimization[C]//International Symposium on Neural Networks, 1995:1942-1948.
[4] Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95:51-67.
[5] 文龙, 蔡绍洪, 焦建军, 等. 求解大规模优化问题的改进鲸鱼优化算法[J]. 系统工程理论与实践, 2017, 37(11):2983-2994.Long W, Cai S H, Jiao J J, et al. Improved whale optimization algorithm for large scale optimization problems[J]. Systems Engineering-Theory & Practice, 2017, 37(11):2983-2994.
[6] 钟明辉, 龙文. 一种随机调整控制参数的鲸鱼优化算法[J]. 科学技术与工程, 2017, 17(12):68-73.Zhong M H, Long W. Whale optimization algorithm based on stochastic adjustment control parameter[J]. Science Technology and Engineering, 2017, 17(12):68-73.
[7] 刘竹松, 李生. 正余混沌双弦鲸鱼优化算法[J]. 计算机工程与应用, 2018, 54(7):159-163. Liu Z S, Li S. Whale optimization algorithm based on chaotic sine cosine operator[J]. Computer Engineering and Applications, 2018, 54(7):159-163.
[8] Sun Y J, Wang X L, Chen Y H, et al. A modified whale optimization algorithm for large-scale globle optimization problems[J]. Expert Systems With Applications, 2018. doi:http://doi.org/10.1016/j.eswa.2018.08.027.
[9] Deep K, Das K N. Quadratic approximation based hybrid genetic algorithm for function optimization[J]. Applied Mathematics & Computation, 2008, 203(1):86-98.
[10] Deep K, Bansal J C. Hybridization of particle swarm optimization with quadratic approximation[J]. OPSEARCH, 2009, 46(1):3-24.
[11] Viswanathan G M, Afanasyev V, Buldyrev S V, et al. Levy flight search patterns of wandering albatrosses[J]. Nature, 1996, 381(6581):413-415.
[12] Yang X S, Deb S, Multiobjective cuckoo search for design optimization[J]. Computers & Operations Research, 2013, 40(6):1616-1624.
[13] Rahnamayan S, Tizhoosh H R, Salama M M, et al. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(1):64-79.
[14] Wang H, Wu Z, Rahnamayan S, et al. Enhancing particle swarm optimization using generalized opposition-based learning[J]. Information Sciences, 2011, 181(20):4699-4714.
[15] 周新宇, 吴志健, 王辉, 等. 一种精英反向学习的粒子群优化算法[J]. 电子学报, 2013, 41(8):1647-1652.Zhou X Y, Wu Z J, Wang H. Elite opposition-based particle swarm optimization[J]. Acta Electronica Sinica, 2013, 41(8):1647-1652.
[16] Rudolph G. Local convergence rates of simple evolutionary algorithms with Cauchy mutations[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(4):249-258.
[17] Lowe D G. Distinctive image features from scale-invari-ant keypoints[C]//International Journal of Computer Vision, 2004, 60(2):91-110.
[18] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA:A gravitational search algorithm[J]. Information Science, 2009, 179:2232-2248.
[19] 郭振洲, 王平, 马云峰, 等. 基于自适应权重和柯西变异的鲸鱼优化算法[J].微电子学与计算机, 2017, 34(9):20-25.Guo Z Z, Wang P, Ma Y F, et al. Whale optimization algorithm based on adaptive weight and cauchy mutation[J]. Microeletronics & Computer, 2017, 34(9):20-25.
[20] 龙文, 武铁斌, 唐斌. 收敛因子非线性变化的鲸鱼优化算法[J]. 兰州理工大学学报, 2017, 43(6):102-107.Long W, Wu T B, Tang B. Whale optimization algorithm with nonlinearly variable convergence factor[J]. Journal of Lanzhou University of Technology, 2017, 43(6):102-107.
[21] 许瑜飞, 钱锋, 杨明磊, 等. 改进鲸鱼优化算法及其在渣油加氢参数优化的应用[J]. 化工学报, 2018, 69(3):891-899.Xu Y F, Qian F, Yang M L, et al. Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters[J]. CIESC Journal, 2018, 69(3):891-899.
[22] Qin A K, Huang V L, Suganthan P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2):398-417.
[23] Wang Y, Cai Z, Zhang Q. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE Transactions on Evolutionary Computation, 2011, 15(1):55-66.
[24] Liang J J, Qin A K, Suganthan P N, et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3):281-295.
[25] Igel C, Hansen N, Roth S. Covariance matrix adaptation for multi-objective optimization[J]. Evolutionary Computation, 2014, 15(1):1-28.
[26] Tuo S, Zhang J, Yong L, et al. A harmony search algorithm for high-dimensional multimodal optimization problems[J]. Digital Signal Processing, 2015, 46:151-163.

基金

国家自然科学基金（71672013）
PDF(2030 KB)

876

Accesses

0

Citation

Detail
段落导航
相关文章

/

〈 〉