Abstract
Nowadays, the speed of solving optimization problems by increasing various issues and the number of variables is critical. The Harris Hawk optimization method is a brand-new, intelligent system that resolves optimization issues by mathematically simulating the natural behavior of hawks. In this study, the Harris Hawks optimization method and the Laplace crossover operator are merged, and a new enhanced Harris Hawks algorithm (LX-HHO) based on the Laplace optimization algorithm is suggested. The purpose of improving this algorithm is to increase the convergence speed while maintaining good accuracy in achieving the optimal solution. The results of the proposed method on the test functions demonstrate that the algorithm has achieved faster convergence than the original version of the Harris Hawks algorithm and other meta-heuristic algorithms. This is due to the algorithm's accuracy in obtaining the best answer in these functions and to a reduction in the evaluation of the optimization function. Also, comparing and evaluating the proposed algorithm with other algorithms by Friedman and MAE test shows the superiority of the LX-HHO algorithm.
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References
Abbasi-khazaei T, Rezvani MH (2022) Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods. Soft Comput 26(18):9287–9322. https://doi.org/10.1007/s00500-022-07245-y
Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021a) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Indus Eng 157:107250. https://doi.org/10.1016/j.cie.2021.107250
Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021b) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng. 376:113609. https://doi.org/10.1016/j.cma.2020.113609
Akgüngör AP, Korkmaz E (2022) Bezier search differential evolution algorithm based estimation models of delay parameter k for signalized intersections. Concurr Comput Pract Exp 34(13):e6931. https://doi.org/10.1002/cpe.6931
Aleem SHA, Zobaa AF, Balci ME, Ismael SM (2019) Harmonic overloading minimization of frequency-dependent components in harmonics polluted distribution systems using Harris Hawks optimization algorithm. IEEE Access 7:100824–100837. https://doi.org/10.1109/ACCESS.2019.2930831
Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715–734. https://doi.org/10.1007/s00500-018-3102-4
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE. p. 4661–4667. https://doi.org/10.1109/CEC.2007.4425083
Du P, Wang J, Hao Y, Niu T, Yang W (2020) A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2. 5 and PM10 forecasting. Appl Soft Comput 96:106620. https://doi.org/10.1016/j.asoc.2020.106620
Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford (ISBN-13: 9780195099713)
Bayraktar Z, Komurcu M, Werner DH (2010) Wind driven optimization (WDO): a novel nature-inspired optimization algorithm and its application to electromagnetics. In: 2010 IEEE antennas and propagation society international symposium. IEEE. p. 1–4. https://doi.org/10.1109/APS.2010.5562213
Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, Rashid ASA (2019) A novel swarm intelligence—Harris Hawks optimization for spatial assessment of landslide susceptibility. Sensors 19(16):3590. https://doi.org/10.3390/s19163590
Chen H, Jiao S, Wang M, Heidari AA, Zhao X (2020) Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. J Clean Prod 244:118778. https://doi.org/10.1016/j.jclepro.2019.118778
de Melo VV, Banzhaf W (2018) Drone Squadron optimization: a novel self-adaptive algorithm for global numerical optimization. Neural Comput Appl 30:3117–3144. https://doi.org/10.1007/s00521-017-2881-3
Deep K, Bansal JC (2009) Optimization of directional over current relay times using Laplace Crossover Particle Swarm Optimization (LXPSO). In: 2009 World congress on nature and biologically inspired computing (NaBIC). p. 288–293. https://doi.org/10.1109/NABIC.2009.5393722
Deep K, Thakur M (2007) A new crossover operator for real coded genetic algorithms. Appl Math Comput 188(1):895–911. https://doi.org/10.1016/j.amc.2006.10.047
Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics. Springer, Boston, pp 227–263. https://doi.org/10.1007/978-1-4419-1665-5_8
Erol OK, Eksin I (2006) A new optimization method: big bang–big crunch. Adv Eng Softw 37(2):106–111. https://doi.org/10.1016/j.advengsoft.2005.04.005
Ewees AA, Elaziz MA (2020) Performance analysis of chaotic multi-verse Harris Hawks optimization: a case study on solving engineering problems. Eng Appl Artif Intell 88:103370. https://doi.org/10.1016/j.engappai.2019.103370
Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183. https://doi.org/10.1016/j.isatra.2014.03.018
Garg V, Deep K (2016) Optimal extraction of bioactive compoundsfrom Gardenia using Laplacian biogeography based optimization. In: Kim J, Jim Z (eds) Harmony search algorithm advances in intelligent systems and computing, vol 382. Springer, Berlin, pp 251–258. https://doi.org/10.1007/978-3-662-47926-1_24
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
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst. 97:849–872. https://doi.org/10.1016/j.future.2019.02.028
Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73. https://doi.org/10.1038/scientificamerican0792-66
Houssein EH, Hosney ME, Oliva D, Mohamed WM, Hassaballah M (2020) A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery. Comput Chem Eng 133:106656. https://doi.org/10.1016/j.compchemeng.2019.106656
Jafari V, Rezvani MH (2021) Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-021-03388-2
Jia H, Lang C, Oliva D, Song W, Peng X (2019) Dynamic Harris Hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens 11(12):1421. https://doi.org/10.3390/rs11121421
Kaboli SHA, Selvaraj J, Rahim N (2017) Rain-fall optimization algorithm: a population based algorithm for solving constrained optimization problems. J Comput Sci 19:31–42. https://doi.org/10.1016/j.jocs.2016.12.010
Kallioras NA, Lagaros ND, Avtzis DN (2018) Pity beetle algorithm–a new metaheuristic inspired by the behavior of bark beetles. Adv Eng Softw 121:147–166. https://doi.org/10.1016/j.advengsoft.2018.04.007
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search,". Acta Mechanica. 213(3):267–289. https://doi.org/10.1007/s00707-009-0270-4
Kayabekir AE, Bekdaş G, Nigdeli SM, Yang X-S (2018) A comprehensive review of the flower pollination algorithm for solving engineering problems. Nature-inspired algorithms and applied optimization. Springer, Cham, pp 171–188. https://doi.org/10.1007/978-3-319-67669-2_8
Korkmaz E (2023) Energy demand estimation in Turkey according to modes of transportation: Bezier search differential evolution and black widow optimization algorithms-based model development and application. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08245-1
Korkmaz E, Akgüngör AP (2018) Flower pollination algorithm approach for the transportation energy demand estimation in Turkey: model development and application. Energy Sour Part B 13(11–12):429–447. https://doi.org/10.1080/15567249.2019.1572835
Korkmaz E, Akgüngör AP (2020) Comparison of artificial bee colony and flower pollination algorithms in vehicle delay models at signalized intersections. Neural Comput Appl 32(8):3581–3597. https://doi.org/10.1007/s00521-018-3670-3
Korkmaz E, Akgüngör AP (2021a) Optimizing of phase plan, sequence and signal timing based on flower pollination algorithm for signalized intersections. Soft Comput 25:4243–4259. https://doi.org/10.1007/s00500-020-05438-x
Korkmaz E, Akgüngör AP (2021b) The forecasting of air transport passenger demands in Turkey by using novel meta-heuristic algorithms. Concurr Comput Pract Exp 33(16):e6263. https://doi.org/10.1002/cpe.6263
Kumar N, Kumar H (2022) A fuzzy clustering technique for enhancing the convergence performance by using improved Fuzzy c-means and Particle Swarm Optimization algorithms. Data Knowl Eng 140:102050. https://doi.org/10.1016/j.datak.2022.102050
Kumar N, Kumar H, Kumar K (2022) A study for plausible third wave of COVID-19 in India through fuzzy time series modelling based on particle swarm optimization and fuzzy c-means. Math Probl Eng. https://www.hindawi.com/journals/mpe/2022/5878268
Liu C (2021) An improved Harris hawks optimizer for job-shop scheduling problem. J Supercomput 77:14090–14129. https://doi.org/10.1007/s11227-021-03834-0
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022
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, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513. https://doi.org/10.1007/s00521-015-1870-7
Moayedi H, Osouli A, Nguyen H, Rashid ASA (2021) A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability. Eng Comput 37:369–379. https://doi.org/10.1007/s00366-019-00828-8
Pham DT, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M (2006) The bees algorithm—a novel tool for complex optimisation problems. In: Intelligent production machines and systems. Elsevier, New York, pp 454–459. https://doi.org/10.1016/B978-008045157-2/50081-X
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1:33–57. https://doi.org/10.1007/s11721-007-0002-0
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
Shahid M, Li JP, Golilarz NA, Addeh A, Khan J, Ul Haq A (2019) Wavelet based image de-noising with optimized thresholding using HHO algorithm. In: 16th international computer conference on wavelet active media technology and information processing. Chengdu, China, pp 6–12. https://doi.org/10.1109/ICCWAMTIP47768.2019.9067590
Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713. https://doi.org/10.1109/TEVC.2008.919004
Singh A (2019) Laplacian whale optimization algorithm. J Int J Syst Assur Eng Manag 10(4):713–730. https://doi.org/10.1007/s13198-019-00801-0
Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global optim. 11(4): 341, 1997. https://www.cp.eng.chula.ac.th/~prabhas//teaching/ec/ec2012/storn_price_de.pdf
Talatahari S, Azizi M (2021) Chaos game optimization: a novel metaheuristic algorithm. Artif Intell Rev 54:917–1004. https://doi.org/10.1007/s10462-020-09867-w
Talatahari S, Azizi M, Tolouei M, Talatahari B, Sareh P (2021a) Crystal structure algorithm (CryStAl): a metaheuristic optimization method. IEEE Access 9:71244–71261. https://doi.org/10.1109/ACCESS.2021.3079161
Talatahari S, Azizi M, Gandomi AH (2021b) Material generation algorithm: a novel metaheuristic algorithm for optimization of engineering problems. Processes 9(5):859. https://doi.org/10.3390/pr9050859
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, Heidelberg, pp 65–74. https://doi.org/10.1007/978-3-642-12538-6_6
Zhang Y, Liu R, Wang X et al (2021) Boosted binary Harris hawks optimizer and feature selection. Eng Comput. 37:3741–3770. https://doi.org/10.1007/s00366-020-01028-5
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Nasab, S.T.M., Abualigah, L. Improve Harris Hawkes optimizer algorithm via Laplace crossover. J Ambient Intell Human Comput 15, 2057–2072 (2024). https://doi.org/10.1007/s12652-023-04734-2
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DOI: https://doi.org/10.1007/s12652-023-04734-2