nach oben

Soft Computing

Erschienen in:

03.11.2016 | Methodologies and Application

Adaptive harmony search with best-based search strategy

verfasst von: Zhaolu Guo, Huogen Yang, Shenwen Wang, Caiying Zhou, Xiaosheng Liu

Erschienen in: Soft Computing | Ausgabe 4/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel APDDE: self-adaptive parameter dynamics differential evolution algorithm

Nächster Artikel Face image retrieval: super-resolution based on sketch-photo transformation

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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):635–672MathSciNetCrossRefMATH

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

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):646–657CrossRef

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

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 94:587–604CrossRef

Chen J, Pan Qk, Li JQ (2012) Harmony search algorithm with dynamic control parameters. Appl Math Comput 219(2):592–604MathSciNetMATH

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

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

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

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

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

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

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

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

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):5896–5911CrossRef

Gao XZ, Wang X, Ovaska SJ, Zenger K (2012) A hybrid optimization method of harmony search and opposition-based learning. Eng Optim 44(8):895–914CrossRef

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):43–58CrossRef

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 92:112–122CrossRef

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

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

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):397–413CrossRef

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 67:140–150CrossRef

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):6263–6285CrossRef

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):273–280CrossRef

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):59–68MathSciNetCrossRef

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

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 232:1166–1182MathSciNetCrossRef

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

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):1859–1880CrossRef

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

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

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

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 228:195–219MathSciNetMATH

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):5337–5355CrossRef

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 63:7–22MathSciNetCrossRefMATH

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

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

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):281–295CrossRef

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):902–910CrossRef

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

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):1818–1831CrossRef

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

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 179:69–87CrossRef

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

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

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 62:237–257CrossRef

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

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 305:14–55CrossRef

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):830–848MathSciNetCrossRefMATH

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

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):398–417CrossRef

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

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

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

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

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

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

Valian E, Tavakoli S, Mohanna S (2014) An intelligent global harmony search approach to continuous optimization problems. Appl Math Comput 232:670–684MathSciNet

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

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):2312–2322CrossRef

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

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

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

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

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

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

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):5788–5803CrossRef

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

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

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

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

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

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

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):1567–1579CrossRef

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

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

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):1556–1564CrossRef

Titel: Adaptive harmony search with best-based search strategy
verfasst von: Zhaolu Guo
Huogen Yang
Shenwen Wang
Caiying Zhou
Xiaosheng Liu
Publikationsdatum: 03.11.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 4/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2424-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2018

A unifying survey on weighted logics and weighted automata

Research on the measurement and evaluation of trusted cloud service

On decidability of recursive weighted logics

Self-adaptive differential evolution algorithm with hybrid mutation operator for parameters identification of PMSM

Characterizations of recognizable weighted tree languages by logic and bimorphisms

The importance of implementation details and parameter settings in black-box optimization: a case study on Gaussian estimation-of-distribution algorithms and circles-in-a-square packing problems

Premium Partner