An Introduction to Harmony Search Optimization Method by Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger

By Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger

This short presents an in depth creation, dialogue and bibliographic overview of the nature1-inspired optimization set of rules known as concord seek. It makes use of quite a few simulation effects to illustrate some great benefits of concord seek and its variations and likewise their drawbacks. The authors exhibit how weaknesses will be amended through hybridization with different optimization tools. The concord seek approach with purposes can be of worth to researchers in computational intelligence in demonstrating the cutting-edge of study on an set of rules of present curiosity. It additionally is helping researchers and practitioners of electric and laptop engineering extra normally in acquainting themselves with this technique of vector-based optimization.

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S. Kulluk, L. Ozbakir, A. Baykasoglu, Training neural networks with harmony search algorithms for classification problems. Eng. Appl. Artif. Intel. 25(1), 11–19 (2012) 30 4 The Variations of Harmony Search and Its Current Research Trends 27. K. X. Guo, A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int. J. Prod. Econ. 128(2), 614–624 (2010) 28. M. Mirkhani, R. M. Shahri, A. Moayedikia, A novel efficient algorithm for mobile robot localization.

Electr. Power Energy Syst. 53, 482–487 (2013) 14. A. Askarzadeh, A discrete chaotic harmony search-based simulated annealing algorithm for optimum design of PV/wind hybrid system. Sol. Energy 97, 93–101 (2013) 15. B. Wu, C. Qian, W. , Hybrid harmony search and artificial bee colony algorithm for global optimization problems. Comput. Math. Appl. 64(8), 2621–2634 (2012) 16. E. Yazdi, V. T. Haghighat, A new biped locomotion involving arms swing based on neural network with harmony search optimizer.

2 A Hybrid Optimization Method of HS and Opposition-Based Learning 39 Optimal Solution a1 f( x* ) x* b1 f(x ) x f(x ) a1 x f( x* ) a′1 x* k =1 f( x* ) x* b′1 k =2 f(x ) x b′1 k =3 Fig. 1 Opposition-Based Learning Developed by Tizhoosh, the OBL is a new approach to machine intelligence, which has been widely employed in optimization, neural networks training, and reinforcement learning [10]. For dealing with optimization problems, the OBL is based on the utilization of the opposition numbers of the current search directions.

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