Abstract
Harmony search (HS) has shown promising performance in a wide range of real-world applications. However, in many cases, the basic HS exhibits strong exploration ability but weak exploitation capability. In order to enhance the exploitation capability of the basic HS, this paper presents an improved global harmony search with generalized opposition-based learning strategy (GOGHS). In GOGHS, the valuable information from the best harmony is utilized to enhance the exploitation capability. Moreover, the generalized opposition-based learning (GOBL) strategy is incorporated to increase the probability of finding the global optimum. The performance of GOGHS is evaluated on a set of benchmark test functions and is compared with several HS variants. The experimental results show that GOGHS can obtain competitive results.
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References
Abedifar V, Eshghi M (2014) An optimized design of optical networks using evolutionary algorithms. J High Speed Netw 20(1):11–27
Al-Betar MA, Doush IA, Khader AT, Awadallah MA (2012) Novel selection schemes for harmony search. Appl Math Comput 218(10):6095–6117
Al-Betar MA, Awadallah MA, Khader AT, Abdalkareem ZA (2015) Island-based harmony search for optimization problems. Expert Syst Appl 42(4):2026–2035
Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Glob Optim 31(4):635–672
Bindiya TS, Elias E (2014) Metaheuristic algorithms for the design of multiplier-less non-uniform filter banks based on frequency response masking. Soft Comput 18(8):1529–1547
Castelli M, Silva S, Manzoni L, Vanneschi L (2014) Geometric selective harmony search. Inform Sci 279:468–482
Chen J, Pan Q-K, Li J-Q (2012) Harmony search algorithm with dynamic control parameters. Appl Math Comput 219(2):592–604
Cobos C, Estupiñán D, Pérez J (2011) GHS+ LEM: global-best harmony search using learnable evolution models. Appl Math Comput 218(6):2558–2578
Das S, Mukhopadhyay A, Roy A, Abraham A, Panigrahi BK (2011) Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization. IEEE Trans Syst Man Cybern Part B Cybern 41(1):89–106
El-Abd M (2013) An improved global-best harmony search algorithm. Appl Math Comput 222:94–106
Gao K-Z, Pan Q-K, Li J-Q (2011) Discrete harmony search algorithm for the no-wait flow shop scheduling problem with total flow time criterion. Int J Adv Manuf Technol 56(5–8):683–692
Gao XZ, Wang X, Ovaska SJ, Zenger K (2012) A hybrid optimization method of harmony search and opposition-based learning. Eng Optim 44(8):895–914
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Guo Z, Yue X, Zhang K, Deng C, Liu S (2015) Enhanced social emotional optimisation algorithm with generalised opposition-based learning. Int J Comput Sci Math 6(1):59–68
Khalili M, Kharrat R, Salahshoor K, Sefat MH (2014) Global dynamic harmony search algorithm: GDHS. Appl Math Comput 228:195–219
Kong X, Gao L, Ouyang H, Li S (2015) A simplified binary harmony search algorithm for large scale 0–1 knapsack problems. Expert Syst Appl 42(12):5337–5355
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579
Manjarres D, Del Ser J, Gil-Lopez S, Vecchio M, Landa-Torres I, Lopez-Valcarce R (2013) A novel heuristic approach for distance-and connectivity-based multihop node localization in wireless sensor networks. Soft Comput 17(1):17–28
Omran Mahamed GH, Mahdavi Mehrdad (2008) Global-best harmony search. Appl Math Comput 198(2):643–656
Ou Yang H-B, Gao L-Q, Li S, Kong X, Zou D-X (2014) On the iterative convergence of harmony search algorithm and a proposed modification. Appl Math Comput 247:1064–1095
Ou Yang H-B, Gao L-Q, Li S, Kong X-Y (2015) Improved novel global harmony search with a new relaxation method for reliability optimization problems. Inform Sci 305:14–55
Pan Q-K, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216(3):830–848
Pan Q-K, Wang L, Gao L (2011) A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers. Appl Soft Comput 11(8):5270–5280
Rahnamayan S, Tizhoosh HR, Salama M (2008) Opposition-based differential evolution. IEEE Trans Evolut Comput 12(1):64–79
Sakamoto S, Kulla E, Oda T, Ikeda M, Barolli L, Xhafa F (2014) A comparison study of hill climbing, simulated annealing and genetic algorithm for node placement problem in wmns. J High Speed Netw 20(1):55–66
Salcedo-Sanz S, Pastor-Sánchez A, Del Ser J, Prieto L, Geem ZW (2015) A coral reefs optimization algorithm with harmony search operators for accurate wind speed prediction. Renew Energy 75:93–101
Shahraki A, Ebrahimi SB (2015) A new approach for forecasting enrollments using harmony search algorithm. J Intell Fuzzy Syst Appl Eng Technol 28(1):279–290
Shivaie M, Ameli MT (2014) An implementation of improved harmony search algorithm for scenario-based transmission expansion planning. Soft Comput 18(8):1615–1630
Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation, pp 695–701
Valian E, Tavakoli S, Mohanna S (2014) An intelligent global harmony search approach to continuous optimization problems. Appl Math Comput 232:670–684
Wang C-M, Huang Y-F (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37(4):2826–2837
Wang H, Wu Z, Liu Y, Wang J, Jiang D, Chen L (2009) Space transformation search: a new evolutionary technique. In: Proceedings of the first ACM/SIGEVO summit on genetic and evolutionary computation, pp 537–544
Wang H, Zhijian W, Rahnamayan S, Liu Y, Ventresca M (2011) Enhancing particle swarm optimization using generalized opposition-based learning. Inform Sci 181(20):4699–4714
Wang J, Wu Z, Wang H (2010) Hybrid differential evolution algorithm with chaos and generalized opposition-based learning. Adv Comput Intell 6382:103–111
Wang L, Yang R, Yin X, Niu Q, Pardalos PM, Fei M (2013) An improved adaptive binary harmony search algorithm. Inform Sci 232:58–87
Wang Y, Liu Y, Feng L, Zhu X (2015) Novel feature selection method based on harmony search for email classification. Knowl Based Syst 73:311–323
Xia HG, Wang QZ, Gao LQ (2013) Opposition-based improved harmony search algorithm solve unconstrained optimization problems. Appl Mech Mater 365–366:170–173
Xiang W-L, An M-Q, Li Y-Z, He R-C, Zhang J-F (2014) An improved global-best harmony search algorithm for faster optimization. Expert Syst Appl 41(13):5788–5803
Yadav P, Kumar R, Panda SK, Chang CS (2012) An intelligent tuned harmony search algorithm for optimisation. Inform Sci 196:47–72
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102
Yu S, Zhu S, Ma Y, Mao D (2015) Enhancing firefly algorithm using generalized opposition-based learning. Computing 97(7):741–754
Zhai J, Gao L, Li S (2015) Robust pole assignment in a specified union region using harmony search algorithm. Neurocomputing 155:12–21
Zhang B, Pan Q-K, Zhang X-L, Duan P-Y (2015) An effective hybrid harmony search-based algorithm for solving multidimensional knapsack problems. Appl Soft Comput 29:288–297
Zheng L, Diao R, Shen Q (2015) Self-adjusting harmony search-based feature selection. Soft Comput 19(6):1567–1579
Zou D, Gao L, Jianhua W, Li S (2010) Novel global harmony search algorithm for unconstrained problems. Neurocomputing 73(16):3308–3318
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (Nos. 61462036, 61402481, and 41561091), by the Fund of Natural Science Foundation of Guangdong Province of China (No. 2014A030313454), and by Natural Science Foundation of Jiangxi, China (Nos. 20151BAB217010, 20151BAB201015).
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Guo, Z., Wang, S., Yue, X. et al. Global harmony search with generalized opposition-based learning. Soft Comput 21, 2129–2137 (2017). https://doi.org/10.1007/s00500-015-1912-1
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DOI: https://doi.org/10.1007/s00500-015-1912-1