Concerning the drawbacks of trapping in local optimal solutions and low convergence accuracy of standard Harmony Search (HS) algorithm, a new harmony search algorithm based on Circular Trust Region (CTR), named as CTRHS, was proposed. CTRHS adopted the one-off generation mode of two pitches. Intensive considerations within the circular trust region were interactively conducted in its memory considering process. Adjustment bandwidth was determined by means of the best or worst harmony vector of current Harmony Memory (HM) during the adjusting process of double pitches. The update of HM was achieved by replacing the worst harmony in current HM with the newly generated harmony. Computational experiments were conducted upon 9 benchmark functions to validate the performance of CTRHS. As demonstrated in the results, CTRHS outperforms other 7 reported HS variants in terms of solution quality and convergence efficiency. Moreover, when the parameters of Harmony Memory Size (HMS) and Harmony Memory Considering Rate (HMCR) are respectively equal to 5 and 0.99, it has better performance in searching the global optimal solutions.
刘乐. 基于圆形信赖域的改进和声搜索算法[J]. 计算机应用, 2015, 35(4): 1049-1056.
LIU Le. Improved harmony search algorithm based on circular trust region. Journal of Computer Applications, 2015, 35(4): 1049-1056.
[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] ZOU D, GAO L, WU J, et al. A novel global harmony search algorithm for reliability problems [J]. Computers and Industrial Engineering, 2010, 58(2): 307-316. [3] FOURIE J, MILLS S, GREEN R. Harmony filter: a robust visual tracking system using the improved harmony search algorithm [J]. Image and Vision Computing, 2010, 28(12): 1702-1716. [4] MURREN P, KHANDELWAL K. Design-Driven Harmony Search (DDHS) in steel frame optimization [J]. Engineering Structures, 2014, 59: 798-808. [5] DIAO R, SHEN Q. Feature selection with harmony search [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2012, 42(6): 1509-1523. [6] ASKARZADEH A, ZEBARJADI M. Wind power modeling using harmony search with a novel parameter setting approach [J]. Journal of Wind Engineering and Industrial Aerodynamics, 2014, 135: 70-75. [7] LAYEB A. A hybrid quantum inspired harmony search algorithm for 0-1 optimization problems [J]. Journal of Computational and Applied Mathematics, 2013, 253: 14-25. [8] ZOU D, GAO L, LI S, et al. A novel global harmony search algorithm for task assignment problem [J]. Journal of Systems and Software, 2010, 83(10): 1678-1688. [9] MOHAMMED H, MASRI A, NASSER R S, et al. A harmony search algorithm for nurse rostering problems [J]. Information Sciences, 2013, 233: 126-140. [10] YUAN Y, XU H, YANG J. A hybrid harmony search algorithm for the flexible job shop scheduling problem [J]. Applied Soft Computing, 2013, 13(7): 3259-3272. [11] 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. [12] WANG C, HUANG Y. Self-adaptive harmony search algorithm for optimization [J]. Expert Systems with Applications, 2010, 37(4): 2826-2837. [13] ENAYATIFAR R, YOUSEFI M, ABDULLAH A H, et al. LAHS: a novel harmony search algorithm based on learning automata [J]. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(12): 3481-3497. [14] OMRAN M G H, MAHDAVI M. Global-best harmony search [J]. Applied Mathematics and Computation, 2008, 198(2): 643-656. [15] 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. [16] ZOU D, GAO L, WU J, et al. Novel global harmony search algorithm for unconstrained problems [J]. Neurocomputing, 2010, 73(16/17/18): 3308-3318. [17] CHEN J, PAN Q, LI J. Harmony search algorithm with dynamic control parameters [J]. Applied Mathematics and Computation, 2012, 219(2): 592-604. [18] YADAV P, RAJESH K, PANDA S K, et al. An intelligent tuned harmony search algorithm for optimization [J]. Information Sciences, 2012, 196: 47-72. [19] EL-ABD M. An improved global-best harmony search algorithm [J]. Applied Mathematics and Computation, 2013, 222: 94-106. [20] KHALILI M, KHARRAT R, SALAHSHOOR K, et al. Global dynamic harmony search algorithm: GDHS [J]. Applied Mathematics and Computation, 2014, 228: 195-219. [21] CHAKRABORTY P, ROY G G, DAS S, et al. An improved harmony search algorithm with differential mutation operator [J]. Fundamenta Informticae, 2009, 95(4): 401-426. [22] WU B, QIAN C, NI W, et al. Hybrid harmony search and artificial bee colony algorithm for global optimization problems [J]. Computers and Mathematics with Applications, 2012, 64(8): 2621-2634. [23] KULLUK S. A novel hybrid algorithm combining hunting search with harmony search algorithm for training neural networks [J]. Journal of the Operational Research Society, 2013, 64(5): 748-761. [24] TANG K, LI X, SUGANTHAN P N, et al. Benchmark functions for the CEC 2010 special session and competition on large-scale global optimization [EB/OL].[2013-12-01]. http://sci2s.ugr.es/eamhco/cec2010_functions.pdf.
2015, Vol. 35 
