Skip to main content
SpringerLink
Log in

Adaptive harmony search with best-based search strategy

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

Abstract

Harmony search (HS) is a new evolutionary algorithm inspired by the process of music improvisation. During the past decade, HS has shown excellent performance in many fields. However, its search strategy often demonstrates insufficient exploitation ability when facing some complex practical problems. Moreover, the HS performance is significantly influenced by its control parameters. To enhance the search efficiency, an adaptive harmony search with best-based search strategy (ABHS) is proposed. In the search process, ABHS exploits the beneficial information from the global-best solution to improve the search ability, while it adaptively tunes its control parameters according to the feedback from the search process. Experiments are conducted on a set of classical test functions. The experimental results show that ABHS significantly enhances the search efficiency of HS.

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
Fig. 2

Similar content being viewed by others

References

  • Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Global Optim 31(4):635–672

    Article  MathSciNet  MATH  Google Scholar 

  • Amaya I, Cruz J, Correa R (2015) Harmony search algorithm: a variant with self-regulated fretwidth. Appl Math Comput 266:1127–1152

    Article  MathSciNet  Google Scholar 

  • Brest J, Greiner S, Bošković B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6):646–657

    Article  Google Scholar 

  • Chakraborty P, Roy GG, Das S, Jain D, Abraham A (2009) An improved harmony search algorithm with differential mutation operator. Fundamenta Informaticae 95(4):401–426

    MathSciNet  MATH  Google Scholar 

  • Chauhan A, Saini RP (2016) Discrete harmony search based size optimization of integrated renewable energy system for remote rural areas of uttarakhand state in india. Renew Energy 94:587–604

    Article  Google Scholar 

  • Chen J, Pan Qk, Li JQ (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 

  • Contreras J, Amaya I, Correa R (2014) An improved variant of the conventional harmony search algorithm. Appl Math Comput 227:821–830

    MathSciNet  MATH  Google Scholar 

  • Dai X, Yuan X, Zhang Z (2015) A self-adaptive multi-objective harmony search algorithm based on harmony memory variance. Appl Soft Comput 35:541–557

    Article  Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evolut Comput 6(2):182–197

    Article  Google Scholar 

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

    MATH  Google Scholar 

  • Fogel DB (1994) An introduction to simulated evolutionary optimization. IEEE Trans Neural Netw 5(1):3–14

    Article  Google Scholar 

  • Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  • Gao KZ, Suganthan PN, Pan QK, Tasgetiren MF (2015a) An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time. Int J Prod Res 53(19):5896–5911

    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 

  • Gao XZ, Wang X, Zenger K (2015b) A memetic-inspired harmony search method in optimal wind generator design. Int J Mach Learn Cybern 6(1):43–58

    Article  Google Scholar 

  • García-Segura T, Yepes V, Alcalá J, Pérez-López E (2015) Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges. Eng Struct 92:112–122

    Article  Google Scholar 

  • Geem ZW, Sim KB (2010) Parameter-setting-free harmony search algorithm. Appl Math Comput 217(8):3881–3889

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Gong W, Cai Z, Ling CX, Li H (2011) Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans Syst Man Cybern Part B Cybern 41(2):397–413

    Article  Google Scholar 

  • Gu B, Sheng VS (2016) A robust regularization path algorithm for \(\nu \)-support vector classification. In: IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2016.2527796 (in press)

  • Gu B, Sheng VS, Wang Z, Ho D, Osman S, Li S (2015) Incremental learning for \(\nu \)-support vector regression. Neural Netw 67:140–150

    Article  Google Scholar 

  • Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. In: IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2016.2544779 (in press)

  • Guo Z, Yue X, Zhang K, Wang S, Wu Z (2014) A thermodynamical selection-based discrete differential evolution for the 0–1 knapsack problem. Entropy 16(12):6263–6285

    Article  Google Scholar 

  • Guo Z, Huang H, Deng C, Yue X, Wu Z (2015) An enhanced differential evolution with elite chaotic local search. In: Computational intelligence and neuroscience, Article ID 583759

  • Guo Z, Huang H, Yang H, Wang S, Wang H (2015b) An enhanced gravitational search algorithm for global optimisation. Int J Wirel Mobile Comput 9(3):273–280

    Article  Google Scholar 

  • Guo Z, Yue X, Zhang K, Deng C, Liu S (2015c) Enhanced social emotional optimisation algorithm with generalised opposition-based learning. Int J Comput Sci Math 6(1):59–68

    Article  MathSciNet  Google Scholar 

  • Han KH, Kim JH (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evolut Comput 6(6):580–593

    Article  Google Scholar 

  • Hasan BHF, Doush IA, Al Maghayreh E, Alkhateeb F, Hamdan M (2014) Hybridizing harmony search algorithm with different mutation operators for continuous problems. Appl Math Comput 232:1166–1182

    Article  MathSciNet  Google Scholar 

  • He S, Wu QH, Saunders JR (2009) Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evolut Comput 13(5):973–990

    Article  Google Scholar 

  • Inbarani HH, Bagyamathi M, Azar AT (2015) A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl 26(8):1859–1880

    Article  Google Scholar 

  • Jeddi B, Vahidinasab V (2014) A modified harmony search method for environmental/economic load dispatch of real-world power systems. Energy Convers Manag 78:661–675

    Article  Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  • Kattan A, Abdullah R (2013) A dynamic self-adaptive harmony search algorithm for continuous optimization problems. Appl Math Comput 219(16):8542–8567

    MathSciNet  MATH  Google Scholar 

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948

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

    MathSciNet  MATH  Google Scholar 

  • Kong X, Gao L, Ouyang H, Li S (2015a) A simplified binary harmony search algorithm for large scale 0–1 knapsack problems. Expert Syst Appl 42(12):5337–5355

    Article  Google Scholar 

  • Kong X, Gao L, Ouyang H, Li S (2015b) Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm. Comput Oper Res 63:7–22

    Article  MathSciNet  MATH  Google Scholar 

  • Kumar V, Chhabra JK, Kumar D (2014) Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. J Comput Sci 5(2):144–155

    Article  MathSciNet  Google Scholar 

  • Lam AYS, Li VOK (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evolut Comput 14(3):381–399

    Article  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295

    Article  Google Scholar 

  • Ma T, Zhou J, Tang M, Tian Y, Al-Dhelaan A, Al-Rodhaan M, Lee S (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst 98(4):902–910

    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, Landa-Torres I, Gil-Lopez S, Del Ser J, Bilbao MN, Salcedo-Sanz S, Geem ZW (2013) A survey on applications of the harmony search algorithm. Eng Appl Artif Intell 26(8):1818–1831

    Article  Google Scholar 

  • Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evolut Comput 8(3):204–210

    Article  Google Scholar 

  • Naik B, Nayak J, Behera HS, Abraham A (2016) A self adaptive harmony search based functional link higher order ann for non-linear data classification. Neurocomputing 179:69–87

    Article  Google Scholar 

  • Nekkaa M, Boughaci D (2016) Hybrid harmony search combined with stochastic local search for feature selection. Neural Process Lett 44(1):199–220

    Article  Google Scholar 

  • Niu Q, Zhang H, Li K, Irwin GW (2014a) An efficient harmony search with new pitch adjustment for dynamic economic dispatch. Energy 65:25–43

    Article  Google Scholar 

  • Niu Q, Zhang H, Wang X, Li K, Irwin GW (2014b) A hybrid harmony search with arithmetic crossover operation for economic dispatch. Int J Electr Power Energy Syst 62:237–257

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Ouyang HB, Gao LQ, Li S, Kong XY (2015) Improved novel global harmony search with a new relaxation method for reliability optimization problems. Inf Sci 305:14–55

    Article  Google Scholar 

  • Pan QK, 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

    Article  MathSciNet  MATH  Google Scholar 

  • Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176

    Article  Google Scholar 

  • Papa JP Scheirer W, Cox DD (2015) Fine-tuning deep belief networks using harmony search. Appl Soft Comput 46:875–885

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evolut Comput 8(3):240–255

    Article  Google Scholar 

  • Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178

    Google Scholar 

  • Shiva CK, Mukherjee V (2015) A novel quasi-oppositional harmony search algorithm for automatic generation control of power system. Appl Soft Comput 35:749–765

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Turky AM, Abdullah S (2014) A multi-population harmony search algorithm with external archive for dynamic optimization problems. Inf Sci 272:84–95

    Article  Google Scholar 

  • 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 CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37(4):2826–2837

    Article  Google Scholar 

  • Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013a) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanosci 10(10):2312–2322

    Article  Google Scholar 

  • Wang GG, Gandomi AH, Zhao X, Chu HCE (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–285

    Article  Google Scholar 

  • Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M (2011) Enhancing particle swarm optimization using generalized opposition-based learning. Inf Sci 181(20):4699–4714

    Article  MathSciNet  Google Scholar 

  • Wang H, Sun H, Li C, Rahnamayan S, Pan JS (2013b) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135

    Article  MathSciNet  Google Scholar 

  • Wang H, Wu Z, Rahnamayan S, Sun H, Liu Y, Pan JS (2014) Multi-strategy ensemble artificial bee colony algorithm. Inf Sci 279:587–603

    Article  MathSciNet  MATH  Google Scholar 

  • Wang L, Yang R, Xu Y, Niu Q, Pardalos PM, Fei M (2013c) An improved adaptive binary harmony search algorithm. Inf Sci 232:58–87

    Article  MathSciNet  Google Scholar 

  • Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406

    Article  Google Scholar 

  • Xiang WL, An MQ, Li YZ, He RC, Zhang JF (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. Inf Sci 196:47–72

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yuan X, Zhao J, Yang Y, Wang Y (2014) Hybrid parallel chaos optimization algorithm with harmony search algorithm. Appl Soft Comput 17:12–22

    Article  Google Scholar 

  • Zhan ZH, Zhang J, Li Y, Shi YH (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evolut Comput 15(6):832–847

    Article  Google Scholar 

  • Zhang B, Pan QK, Zhang XL, Duan PY (2015) An effective hybrid harmony search-based algorithm for solving multidimensional knapsack problems. Appl Soft Comput 29:288–297

    Article  Google Scholar 

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958

    Article  Google Scholar 

  • Zhao SZ, Suganthan PN, Pan QK, Tasgetiren MF (2011) Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38(4):3735–3742

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

    Article  Google Scholar 

  • Zheng Y, Jeon B, Xu D, Wu QM, Zhang H (2015b) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):961–973

    Google Scholar 

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

    Article  Google Scholar 

  • Zou D, Gao L, Li S, Wu J (2011) Solving 0–1 knapsack problem by a novel global harmony search algorithm. Appl Soft Comput 11(2):1556–1564

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Nos. 61662029, 61462036, and 41561091), the Natural Science Foundation of Jiangxi, China (Nos. 20151BAB217010 and 20151BAB201015), and the Education Department Scientific Research Foundation of Jiangxi Province, China (No. GJJ14433).

Author information

Authors and Affiliations

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

    Zhaolu Guo, Huogen Yang & Caiying Zhou

  2. School of Information Engineering, Hebei GEO University, Shijiazhuang, 050031, China

    Shenwen Wang

  3. School of Architectural and Surveying and Mapping Engineering, JiangXi University of Science and Technology, Ganzhou, 341000, China

    Xiaosheng Liu

Authors
  1. Zhaolu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huogen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shenwen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caiying Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaosheng Liu
    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 they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain any studies with human participants.

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., Yang, H., Wang, S. et al. Adaptive harmony search with best-based search strategy. Soft Comput 22, 1335–1349 (2018). https://doi.org/10.1007/s00500-016-2424-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2424-3

Keywords

Search

Navigation