Skip to main content
SpringerLink
Log in

Global harmony search with generalized opposition-based learning

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Abedifar V, Eshghi M (2014) An optimized design of optical networks using evolutionary algorithms. J High Speed Netw 20(1):11–27

    Google Scholar 

  • Al-Betar MA, Doush IA, Khader AT, Awadallah MA (2012) Novel selection schemes for harmony search. Appl Math Comput 218(10):6095–6117

    MATH  Google Scholar 

  • Al-Betar MA, Awadallah MA, Khader AT, Abdalkareem ZA (2015) Island-based harmony search for optimization problems. Expert Syst Appl 42(4):2026–2035

    Article  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • Castelli M, Silva S, Manzoni L, Vanneschi L (2014) Geometric selective harmony search. Inform Sci 279:468–482

    Article  MathSciNet  MATH  Google Scholar 

  • Chen J, Pan Q-K, Li J-Q (2012) Harmony search algorithm with dynamic control parameters. Appl Math Comput 219(2):592–604

    MathSciNet  MATH  Google Scholar 

  • Cobos C, Estupiñán D, Pérez J (2011) GHS+ LEM: global-best harmony search using learnable evolution models. Appl Math Comput 218(6):2558–2578

    MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • El-Abd M (2013) An improved global-best harmony search algorithm. Appl Math Comput 222:94–106

    MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Khalili M, Kharrat R, Salahshoor K, Sefat MH (2014) Global dynamic harmony search algorithm: GDHS. Appl Math Comput 228:195–219

    MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • Omran Mahamed GH, Mahdavi Mehrdad (2008) Global-best harmony search. Appl Math Comput 198(2):643–656

    MathSciNet  MATH  Google Scholar 

  • 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

    MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • Rahnamayan S, Tizhoosh HR, Salama M (2008) Opposition-based differential evolution. IEEE Trans Evolut Comput 12(1):64–79

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Shivaie M, Ameli MT (2014) An implementation of improved harmony search algorithm for scenario-based transmission expansion planning. Soft Comput 18(8):1615–1630

    Article  Google Scholar 

  • 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

    MathSciNet  Google Scholar 

  • Wang C-M, Huang Y-F (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37(4):2826–2837

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • Xia HG, Wang QZ, Gao LQ (2013) Opposition-based improved harmony search algorithm solve unconstrained optimization problems. Appl Mech Mater 365–366:170–173

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Yadav P, Kumar R, Panda SK, Chang CS (2012) An intelligent tuned harmony search algorithm for optimisation. Inform Sci 196:47–72

    Article  Google Scholar 

  • Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  • Yu S, Zhu S, Ma Y, Mao D (2015) Enhancing firefly algorithm using generalized opposition-based learning. Computing 97(7):741–754

    Article  MathSciNet  MATH  Google Scholar 

  • Zhai J, Gao L, Li S (2015) Robust pole assignment in a specified union region using harmony search algorithm. Neurocomputing 155:12–21

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Zheng L, Diao R, Shen Q (2015) Self-adjusting harmony search-based feature selection. Soft Comput 19(6):1567–1579

    Article  Google Scholar 

  • Zou D, Gao L, Jianhua W, Li S (2010) Novel global harmony search algorithm for unconstrained problems. Neurocomputing 73(16):3308–3318

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, 341000, Ganzhou, China

    Zhaolu Guo, Xuezhi Yue & Huogen Yang

  2. School of Information Engineering, Shijiazhuang University of Economics, 050031, Shijiazhuang, China

    Shenwen Wang

Authors
  1. Zhaolu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shenwen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuezhi Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huogen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaolu Guo.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-1912-1

Keywords

Search

Navigation