[1]LI Yanhai,TUO Shouheng.Hybrid algorithm based on harmony search and differential evolution for solving multi-modal complex problems[J].CAAI Transactions on Intelligent Systems,2018,13(2):281-289.[doi:10.11992/tis.201612030]
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Hybrid algorithm based on harmony search and differential evolution for solving multi-modal complex problems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 2 Page number: 281-289 Column: 学术论文—智能系统 Public date: 2018-04-15
Title:
Hybrid algorithm based on harmony search and differential evolution for solving multi-modal complex problems
Author(s):
LI Yanhai;  TUO Shouheng
School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China
Keywords:
harmony search; differential evolution; hybrid mechanism; success rate; mutation strategy; multimodal optimization problem
CLC:
TP391
DOI:
10.11992/tis.201612030
Abstract:
This paper presents a hybrid algorithm (HHSDE) based on harmony search and differential evolution for solving multi-modal complex optimization. In different evolution stages, HHSDE algorithm self-adaptively selects harmony search (HS) or differential evolution (DE) algorithm as the means of updating the next generation of population on basis of the cumulative success rate of weighted update, in addition, it changes the mutation strategy of differential evolution (DE) algorithm for balancing the global and local search ability of the differential evolution (DE) algorithm. To investigate the performance of HHSDE, ten multi-modal Benchmark functions were tested. The experimental results, compared with other algorithms by Wilcoxon rank sum test, indicate that HHSDE algorithm has the advantages such as fast convergence speed, high solution precision and excellent stability.
References:
[1] TRELEA I C. The particle swarm optimization algorithm: convergence analysis and parameter selection[J]. Information processing letters, 2003, 85(6): 317-325.
[2] DAS S, SUGANTHAN P N. Differential evolution: a survey of the state-of-the-art[J]. IEEE transactions on evolutionary computation, 2011, 15(1): 4-31.
[3] DASGUPTA K, MANDAL B, DUTTA P, et al. A genetic algorithm (GA) based load balancing strategy for cloud computing[J]. Procedia technology, 2013, 10: 340-347.
[4] DEEPA O, SENTHILKUMAR A. Swarm intelligence from natural to artificial systems: ant colony optimization[J]. International journal on applications of graph theory in wireless Ad hoc networks and sensor networks, 2016, 8(1): 9-17.
[5] MAHDAVI M, FESANGHARY M, DAMANGIR E. An improved harmony search algorithm for solving optimization problems[J]. Applied mathematics and computation, 2007, 188(2): 1567-1579.
[6] ZOU Dexuan, GAO Liqun, WU Jianhua, et al. Novel global harmony search algorithm for unconstrained problems[J]. Neurocomputing, 2010, 73(16/17/18): 3308-3318.
[7] 夏红刚, 欧阳海滨, 高立群. 多子群混合和声搜索算法[J]. 东北大学学报: 自然科学版, 2015, 36(2): 171-175, 187.
XIA Honggang, OUYANG Haibin, GAO Liqun. Multiple-sub-groups hybrid harmony search algorithm[J]. Journal of Northeastern university: natural science, 2015, 36(2): 171-175, 187.
[8] 拓守恒, 雍龙泉, 邓方安. 动态调整策略改进的和声搜索算法[J]. 智能系统学报, 2015, 10(2): 307-315.
TUO Shouheng, YONG Longquan, DENG Fang’an. Dynamic adjustment strategy for improving the harmony search algorithm[J]. CAAI transactions on intelligent systems, 2015, 10(2): 307-315.
[9] 夏红刚, 欧阳海滨, 高立群, 等. 全局竞争和声搜索算法[J]. 控制与决策, 2016, 31(2): 310-316.
XIA Honggang, OUYANG Haibin, GAO Liqun, et al. Global competitive harmony search algorithm[J]. Control and decision, 2016, 31(2): 310-316.
[10] WANG Yong, CAI Zixing, ZHANG Qingfu. Differential evolution with composite trial vector generation strategies and control parameters[J]. IEEE transactions on evolutionary computation, 2011, 15(1): 55-66.
[11] 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.
[12] 李荣雨, 陈庆倩, 陈菲尔. 改进种群多样性的双变异差分进化算法[J]. 运筹学学报, 2017, 21(1): 44-54.
LI Rongyu, CHEN Qingqian, CHEN Feier. Differential evolution algorithm with double mutation strategies for improving population diversity[J]. Operations research transactions, 2017, 21(1): 44-54.
[13] WANG Ling, LI Lingpo. A coevolutionary differential evolution with harmony search for reliability-redundancy optimization[J]. Expert systems with applications, 2012, 39(5): 5271-5278.
[14] ARUL R, RAVI G, VELUSAMI S. Chaotic self-adaptive differential harmony search algorithm based dynamic economic dispatch[J]. International journal of electrical power and energy systems, 2013, 50: 85-96.
[15] 雍龙泉, 刘三阳, 张建科, 等. 基于差分算子的和声搜索算法求解非线性l1模极小化问题[J]. 兰州大学学报: 自然科学版, 2013, 49(4): 541-546.
YONG Longquan, LIU Sanyang, ZHANG Jianke, et al. Improved harmony search algorithm with differential operator for nonlinear l1 norm minimization problems[J]. Journal of Lanzhou university: natural sciences, 2013, 49(4): 541-546.
[16] YONG Longquan, LIU Sanyang. An improved harmony search algorithm with differential operator for absolute value equation[J]. ICIC express letters, 2014, 8(4): 1151-1157.
[17] ABEDINPOURSHOTORBAN H, HASAN S, SHAMSUDDIN S M, et al. A differential-based harmony search algorithm for the optimization of continuous problems[J]. Expert systems with applications, 2016, 62: 317-332.
[18] TANG K, YAO X, SUGANTHAN P N, et al. Benchmark functions for the CEC’2008 special session and competition on large scale global optimization[R]. Technical Report. China: Nature Inspired Computation and Applications Laboratory, USTC, 2007.
[19] TANG Ke, LI Xiaohong, SUGANTHAN P N, et al. Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization[R]. Technical Report. Nanyang: Nature Inspired Computation and Applications Laboratory, USTC, China and Nanyang Technological University, 2009.
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