山东大学学报 (工学版) ›› 2021, Vol. 51 ›› Issue (2): 47-56.doi: 10.6040/j.issn.1672-3961.0.2020.395
• • 上一篇
周恺卿,李航程,莫礼平
ZHOU Kaiqing, LI Hangcheng, MO Liping
摘要: 针对传统和声算法收敛速度慢和搜索精度低等固有缺点,提出一种改进的自适应全局最优和声搜索算法。在即兴创作方案中,带宽由当前和声里的最优和声变量和最差和声变量之差表示,使得带宽具有针对具体情况的自适应能力,并且每次保存最优和声中一个随机和声变量。在产生的随机数大于和声记忆库存储考虑概率时,利用种群内差分随机生成一个和声变量。为了提高和声搜索算法的搜索能力,在即兴创作结束后产生一个新的和声的同时,再从当前种群中的最小和声到最大和声之间随机产生一个和声,然后将两个新产生和声中误差小的和声进入更新和声记忆库阶段。将所提出的算法与3个改进和声搜索算法在13个测试函数上进行对比。试验结果表明,提出的改进算法具有更好的全局搜索能力和收敛速度。
中图分类号:
[1] GEEM Z W, KIM J H, LOGANATHAN G V. A new heuristic optimization algorithm: harmony search[J]. Simulation, 2001, 76(2):60-68. [2] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197. [3] PUGH J, SEGAPELLI L, MARTINOLI A. Ant colony optimization and swarm intelligence[J]. Lecture Notes in Computer Science, 2004, 49(8):767-771. [4] 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. [5] LEE K S, GEEM Z W. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice[J]. Computer Methods in Applied Mechanics and Engineering, 2005, 194(36):3902-3933. [6] KULLUK S, OZBAKIR L, BAYKASOGLU A. Training neural networks with harmony search algorithms for classification problems[J]. Engineering Applications of Artificial Intelligence, 2012, 25(1):11-19. [7] MOAYEDIKIA A, ONG K L, BOO Y L, et al. Feature selection for high dimensional imbalanced class data using harmony search[J]. Engineering Applications of Artificial Intelligence, 2017, 57:38-49. [8] BEIGZADEH M M B, ABOLGHASEM M S. Harmony search path detection for vision based automated guided vehicle[J]. Robotics and Autonomous Systems, 2018, 107:156-166. [9] ROSLE M S, MOHAMAD M S, CHOON Y W, et al. A hybrid of particle swarm optimization and harmony search to estimate kinetic parameters in arabidopsis thaliana[J]. Processes, 2020, 8(8):1-12. [10] OMRAN M G H, MAHDAVI M. Global-best harmony search[J]. Applied Mathematics and Computation, 2008, 198(2):643-656. [11] PAN Q K, SUGANTHAN P N, TASGETIREN M F, et al. A self-adaptive global best harmony search algorithm for continuous optimization problems[J]. Applied Mathematics and Computation, 2010, 216(3): 830-848. [12] ZOU Dexuan, GAO Liqun. A novel global harmony search algorithm for task assignment problem[J]. Journal of Systems and Software, 2010, 83(10):1678-1688. [13] ZOU Dexuan, GAO Liqun. Novel global harmony search algorithm for unconstrained problems[J]. Neurocomputing, 2010, 73(16):3308-3318. [14] WANG Lin, HU Huanlin, LIU Rui, et al. An improved differential harmony search algorithm for function optimization problems[J]. Soft Computing, 2019, 23(13):4827-4852. [15] QIN A K, FORBES F. Harmony search with differential mutation based pitch adjustment[C] //Proceedings of the 13th annual conference on genetic and evolutionary computation.Berlin, Germany: ACM, 2011. [16] ZHU Qidan, TANG Xiangmeng, LI Yong. An improved differential-based harmony search algorithm with linear dynamic domain[J]. Knowledge-Based Systems, 2020, 187:1-14. [17] LI Hui, SHIH P C, ZHOU Xiaozhao. An improved novel global harmony search algorithm based on selective acceptance[J]. Appl Sci, 2020, 10(6):1-20. [18] JAMIL M, YANG X S. A literature survey of benchmark functions for global optimization problems[J]. International Journal of Mathematical Modelling & Numerical Optimisation, 2013, 4(2):150-194. [19] LAGUNA M, MARTI R. Experimental testing of advanced scatter search designs for global optimization of multimodal functions[J]. Journal of Global Optimi-zation, 2005, 33(2):235-255. [20] GUO Zhaolu, WANG Shenwen, YUE Xuezhi, et al. Global harmony search with generalized opposition-based learning[J]. Soft Computing, 2017, 21(8):2129-2137. |
[1] | 武慧虹,钱淑渠,刘衍民,徐国峰,郭本华. 精英克隆局部搜索的多目标动态环境经济调度差分进化算法[J]. 山东大学学报 (工学版), 2021, 51(1): 11-23. |
[2] | 程春蕊,毛北行. 一类非线性混沌系统的自适应滑模同步[J]. 山东大学学报 (工学版), 2020, 50(5): 1-6. |
[3] | 王春彦,邸金红,毛北行. 基于新型趋近律的参数未知分数阶Rucklidge系统的滑模同步[J]. 山东大学学报 (工学版), 2020, 50(4): 40-45. |
[4] | 刘保成,朴燕,宋雪梅. 联合检测的自适应融合目标跟踪[J]. 山东大学学报 (工学版), 2020, 50(3): 51-57. |
[5] | 闫威,张达敏,张绘娟,辛梓芸,陈忠云. 基于混合决策的改进鸟群算法[J]. 山东大学学报 (工学版), 2020, 50(2): 34-43. |
[6] | 张胜男,王雷,常春红,郝本利. 基于三维剪切波变换和BM4D的图像去噪方法[J]. 山东大学学报 (工学版), 2020, 50(2): 83-90. |
[7] | 苏佳林,王元卓,靳小龙,程学旗. 自适应属性选择的实体对齐方法[J]. 山东大学学报 (工学版), 2020, 50(1): 14-20. |
[8] | 曹小洁,李小华,刘辉. 一类非仿射非线性大系统的结构在线扩展[J]. 山东大学学报 (工学版), 2020, 50(1): 35-48. |
[9] | 刘鸿斌,宋留. 废水处理过程的典型相关分析建模方法研究[J]. 山东大学学报 (工学版), 2020, 50(1): 101-108. |
[10] | 刘美珍,周风余,李铭,王玉刚,陈科. 基于模型不确定补偿的轮式移动机器人反演复合控制[J]. 山东大学学报 (工学版), 2019, 49(6): 36-44. |
[11] | 马川,刘彦呈,刘厶源,张勤进. 考虑未知死区非线性的自适应模糊神经UUV航迹跟踪控制[J]. 山东大学学报 (工学版), 2019, 49(3): 47-56. |
[12] | 李进,李二超. 基于正态分布和自适应变异算子的ε截断算法[J]. 山东大学学报 (工学版), 2019, 49(2): 47-53. |
[13] | 刘洪铭,曾鸿雁,周伟,王涛. 基于改进粒子群算法作业车间调度问题的优化[J]. 山东大学学报 (工学版), 2019, 49(1): 75-82. |
[14] | 公冶小燕,林培光,任威隆. 基于Grefenstette编码和2-opt优化的遗传算法[J]. 山东大学学报 (工学版), 2018, 48(6): 19-26. |
[15] | 李尧,王志海,孙艳歌,张伟. 一种基于深度属性加权的数据流自适应集成分类算法[J]. 山东大学学报 (工学版), 2018, 48(6): 44-55, 66. |
|