Abstract
Harmony search (HS) is a type of population-based optimization algorithm that is introduced based on the idea of musical instruments being tuned to obtain the best harmony state. Several versions of HS have been presented, with global harmony search (GHS) considered one of the most popular. Although GHS is efficient in solving various optimization problems, a new position updating mechanism has been added to improve its efficiency and help it avoid getting stuck in local minima. The novel algorithm proposed in this paper is called the intersect mutation global harmony search algorithm (IMGHSA), which has been tested and evaluated on a set of well-known benchmark functions. The IMGHSA is compared with several improved variants of the HS algorithm, such as the basic version of harmony search (HS), improved differential harmony search, generalized opposition-based learning with global harmony search , and novel global harmony search. The experimental results show that the proposed IMGHSA performs better than the state-of-the-art HS variants and has a more robust convergence when optimizing objective functions in terms of the solution accuracy and efficiency.
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Abbreviations
- BW:
-
Bandwidth vector
- GHS:
-
Global-based harmony search
- GOGHS:
-
Generalized opposition-based learning with global harmony search
- HMCR:
-
Harmony memory consideration rate
- \(\mathrm{HMCR}_{\mathrm{min}}\) :
-
Minimum harmony memory consideration rate
- \(\mathrm{HMR}_{\mathrm{max}}\) :
-
Maximum harmony memory consideration rate
- HMS:
-
Harmony memory size
- HS:
-
Harmony search
- IDHS:
-
Improved differential harmony search
- IMGHSA:
-
Intersect mutation global harmony search algorithm
- LB:
-
Lower bound of optimization problem
- NGHS:
-
Novel global harmony search
- NI:
-
Maximum number of iteration
- NV:
-
The number of decision variables
- \(\mathrm{OF}^{\mathrm{new}}\) :
-
Fitness of new vector
- \(\mathrm{OF}^{\mathrm{worst}}\) :
-
Fitness of the worst vector
- PAR:
-
Pitch adjusting rate
- \(\mathrm{PAR}_{\mathrm{max}}\) :
-
Maximum pitch adjusting rate
- \(\mathrm{PAR}_{\mathrm{min}}\) :
-
Minimum pitch adjusting rate
- Pm:
-
Genetic mutation probability
- r1, r2:
-
a random number between 0 and 1
- UB:
-
Upper bound of optimization problem
- \(x^{\mathrm{new}}\) :
-
A new harmony vector
- \(x^{}\) :
-
Harmony vector
References
Al-Omoush AA, Alsewari AA, Alamri HS, Zamli KZ (2019) Comprehensive review of the development of the harmony search algorithm and its applications. IEEE Access 7:14233–14245. https://doi.org/10.1109/ACCESS.2019.2893662
Amini F, Ghaderi P (2013) Hybridization of harmony search and ant colony optimization for optimal locating of structural dampers. Appl Soft Comput 13(5):2272–2280. https://doi.org/10.1016/j.asoc.2013.02.001
Atta S, Sinha Mahapatra PR, Mukhopadhyay A (2019) Solving tool indexing problem using harmony search algorithm with harmony refinement. Soft Comput 23:7407–7423. https://doi.org/10.1007/s00500-018-3385-5
Degertekin SO, Hayalioglu MS, Gorgun H (2009) Optimum design of geometrically non-linear steel frames with semi-rigid connections using a harmony search algorithm. Steel Compos Struct 9(6):535–555. https://doi.org/10.12989/scs.2009.9.6.535
Gao K, Zhang Y, Sadollah A, Su R (2016) Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search. Appl Soft Comput 48:359–372. https://doi.org/10.1016/j.asoc.2016.07.029
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. https://doi.org/10.1177/003754970107600201
Geem Z. W, Tseng C. L, Park Y (2005) Harmony search for generalized orienteering problem: best touring in China. In: Lecture Notes in Computer Science, vol 3612, No. PART III, pp 741–750, https://doi.org/10.1007/11539902_91
Gheisarnejad M (2018) An effective hybrid harmony search and cuckoo optimization algorithm based fuzzy PID controller for load frequency control. Appl Soft Comput 65:121–138. https://doi.org/10.1016/j.asoc.2018.01.007
Gholami J, Mohammadi S (2018) A novel combination of bees and firefly algorithm to optimize continuous problems. In: 8th International Conference on Computer and Knowledge Engineering, ICCKE 2018, pp 40–46, https://doi.org/10.1109/ICCKE.2018.8566263
Guo Z, Wang S, Yue X, Yang H (2017) Global harmony search with generalized opposition-based learning. Soft Comput 21(8):2129–2137. https://doi.org/10.1007/s00500-015-1912-1
Guo Z, Wang S, Yue X et al (2017) Global harmony search with generalized opposition-based learning. Soft Comput 21:2129–2137. https://doi.org/10.1007/s00500-015-1912-1
Guo Z, Yang H, Wang S et al (2018) Adaptive harmony search with best-based search strategy. Soft Comput 22:1335–1349. https://doi.org/10.1007/s00500-016-2424-3
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82(9–10):781–798. https://doi.org/10.1016/j.compstruc.2004.01.002
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579. https://doi.org/10.1016/j.amc.2006.11.033
Marinakis Y, Marinaki M, Dounias G (2008) Particle swarm optimization for pap-smear diagnosis. Exp Syst Appl 35(4):1645–1656. https://doi.org/10.1016/j.eswa.2007.08.089
Mirjalili S, Lewis A (2016) The Whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Nazari-Heris M, Mohammadi-Ivatloo B, Asadi S, Kim JH, Geem ZW (2019) Harmony search algorithm for energy system applications: an updated review and analysis. J Exp Theor Artif Intel Taylor Francis 31(5):723–749. https://doi.org/10.1080/0952813X.2018.1550814
Ouaddah A, Boughaci D (2016) Harmony search algorithm for image reconstruction from projections. Appl Soft Comput 46:924–935. https://doi.org/10.1016/j.asoc.2016.02.031
Ouyang H, Gao L, Li S, Kong X, Wang Q, Zou D (2017) Improved harmony search algorithm: LHS. Appl Soft Comput 53:133–167. https://doi.org/10.1016/J.ASOC.2016.12.042
Ouyang H, Wu W, Zhang C et al (2019) Improved harmony search with general iteration models for engineering design optimization problems. Soft Comput 23:10225–10260. https://doi.org/10.1007/s00500-018-3579-x
Qing A (2006) Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problems. IEEE Trans Geosci Remote Sens 44(1):116–125. https://doi.org/10.1109/TGRS.2005.859347
Sayah S, Hamouda A, Bekrar A (2014) Efficient hybrid optimization approach for emission constrained economic dispatch with nonsmooth cost curves. Int J Electr Power Energy Syst 56:127–139. https://doi.org/10.1016/j.ijepes.2013.11.001
Serrurier M, Prade H (2008) Improving inductive logic programming by using simulated annealing. Inf Sci 178(6):1423–1441. https://doi.org/10.1016/j.ins.2007.10.015
Valian E, Tavakoli S, Mohanna S (2014) An intelligent global harmony search approach to continuous optimization problems. Appl Math Comput 232:670–684. https://doi.org/10.1016/j.amc.2014.01.086
Wang L et al (2019) New fruit fly optimization algorithm with joint search strategies for function optimization problems. Knowl Based Syst 176:77–96. https://doi.org/10.1016/j.knosys.2019.03.028
Wang L, Hu H, Liu R, Zhou X (2018) An improved differential harmony search algorithm for function optimization problems. Soft Comput. https://doi.org/10.1007/s00500-018-3139-4
Yusup N, Zain A.M, Latib A.A (2019) A review of Harmony Search algorithm-based feature selection method for classification. Journal of Physics: Conference Series, IOP Publishing, vol 1192, No. 012038, https://doi.org/10.1088/1742-6596/1192/1/012038
Zou D, Gao L, Wu J, Li S (2010) Novel global harmony search algorithm for unconstrained problems. Neurocomputing 73(16–18):3308–3318. https://doi.org/10.1016/j.neucom.2010.07.010
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Gholami, J., Ghany, K.K.A. & Zawbaa, H.M. A novel global harmony search algorithm for solving numerical optimizations. Soft Comput 25, 2837–2849 (2021). https://doi.org/10.1007/s00500-020-05341-5
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DOI: https://doi.org/10.1007/s00500-020-05341-5