Self-adapted Harmony Search Algorithm with Opposed Competition and Its Optimization
OUYANG Hai-Bin1, GAO Li-Qun1, KONG Xian-Yong1, ZOU De-Xuan2
1.College of Information Science and Engineering, Northeastern University, Shenyang 110819 2.School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116
Abstract：A self-adapted harmony search algorithm with opposed competition (SHSOC) is proposed. The blindness of bandwidth setting of harmony search algorithm is analyzed.The adaptive bandwidth adjustment is employed. Meanwhile, the superiority of the opposed learning strategy is integrated into the proposed algorithm, and the competition selection mechanism of end elimination is established to further improve global search ability and keep the algorithm from falling into local optima. The proposed algorithm is tested on several classic functions to evaluate the performance. The numerical results show the superiority of SHSOC in accuracy and robustness compared with harmony search algorithm and some state-of-the-art harmony search variants. Moreover, SHSOC can solve the optimization problems of the heat exchanger and the speed reducer design, and the results show that SHSOC is better than any other algorithm.
[1] Geem Z W, Kim J H, Loganathan G V. A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 2001, 76(2): 60-68 [2] Sivasubramani S, Swarup K S. Environmental/Economic Dispatch Using Multi-objective Harmony Search Algorithm. Electric Power Systems Research, 2011, 81(9): 1778-1785 [3] Sharma K D, Chatterjee A, Rakshit A. Design of a Hybrid Stable Adaptive Fuzzy Controller Employing Lyapunov Theory and Harmony Search Algorithm. IEEE Trans on Control Systems Technology, 2010, 18(6): 1440-1447 [4] Ayvaz M T. Application of Harmony Search Algorithm to the Solution of Groundwater Management Models. Advances in Water Resources, 2009, 32(6): 916-924 [5] Zou D X, Gao L Q, Wu J H, et al. A Novel Global Harmony Search Algorithm for Reliability Problems. Computers and Industrial Engineering, 2010, 58(2): 307-316 [6] Mahdavi M, Fesanghary M, Damangir E. An Improve Harmony Search Algorithm for Solving Optimization Problems. Applied Mathematics and Computation, 2007, 188(2): 1567-1579 [7] Omran M G H, Mahdavi M. Global-Best Harmony Search. Applied Mathematics and Computation, 2008, 198(2): 643-656 [8] Wang C M, Huang Y F. Self-adaptive Harmony Search Algorithm for Optimization. Expert Systems with Applications, 2010, 37(4): 2826-2837 [9] Pan Q K, Suganthan P N, Tasgetiren M F, et al. A Self-adaptive Global Best Harmony Search Algorithm for Continuous Optimization Problems. Applied Mathematics and Computation, 2010, 216(3): 830-848 [10] Chen J, Pan Q K, Li J Q. Harmony Search Algorithm with Dynamic Control Parameters. Applied Mathematics and Computation, 2012, 219(2): 592-604 [11] Yadav P, Kumar R, Panda S, et al. An Intelligent Tuned Harmony Search Algorithm for Optimization. Information Sciences, 2012, 196: 47-72 [12] Zou D X, Gao L Q, Wu J H, et al. Novel Global Harmony Search Algorithm for Unconstrained Problems. Neurocomputing, 2010, 73 (16/17/18): 3308-3318 [13] Tizhoosh H R. Opposition-Based Learning: A New Scheme for Machine Intelligence // Proc of the International Conference on Computational Intelligence for Modeling Control and Automation. Vienna, Austria, 2005: 695-701 [14] Rahnamayan S, Tizhoosh H R, Salama M M. Opposition-Based Differential Evolution. IEEE Trans on Evolutionary Computation, 2008, 12(1): 64-79 [15] Wang H, Liu H, Lui Y. et al. Opposition-Based Particle Swarm Algorithm with Cauchy Mutation // Proc of the IEEE Congress on Evolutionary Computation. Singapore, Singapore, 2007: 4750-4756 [16] Malisia A R, Tizhoosh H R. Applying Opposition-Based Ideas to the Ant Colony System // Proc of the IEEE Swarm Intelligence Symposium. Honolulu, USA, 2007: 182-189 [17] Wang S W, Ding L X, Xie D T, et al. Group Search Optimizer Applying Opposition-Based Learning. Computer Science, 2012, 39(9): 183-187 (in Chinese) (汪慎文,丁立新,谢大同,等.应用反向学习策略的群搜索优化算法.计算机科学, 2012, 39(9): 183-187) [18] Joines J A, Houck C R. On the Use of Non-stationary Penalty Functions to Solve Nonlinear Constrained Problems with GA's // Proc of the 1st IEEE Conference on Evolutionary Computation. Orlando, USA, 1994, Ⅱ: 579-584 [19] Deb K. An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering, 2000, 186(2/3/4): 311-318 [20] Majid J, Esmaile K. Two Improved Harmony Search Algorithms for Solving Engineering Optimization Problems. Communication in Nonlinear Science and Numerical Simulation, 2010, 15(11): 3316-3331 [21] Li H L, Papalambros P. A Production System for Use of Global Optimization Knowledge. Journal of Mechanisms, Transmissions and Automation in Design, 1985, 107(2): 277-284
2014, Vol. 27 
