Abstract
Harris hawks optimization (HHO) is a recently developed meta-heuristic optimization algorithm based on hunting behavior of Harris hawks. Similar to other meta-heuristic algorithms, HHO tends to be trapped in low diversity, local optima and unbalanced exploitation ability. In order to improve the performance of HHO, a novel quasi-reflected Harris hawks algorithm (QRHHO) is proposed, which combines HHO algorithm and quasi-reflection-based learning mechanism (QRBL) together. The improvement includes two parts: the QRBL mechanism is introduced firstly to increase the population diversity in the initial stage, and then, QRBL is added in each population position update to improve the convergence rate. The proposed method will also be helpful to control the balance between exploration and exploitation. The performance of QRHHO has been tested on twenty-three benchmark functions of various types and dimensions. Through comparison with the basic HHO, HHO combined with opposition-based learning mechanism and HHO combined with quasi-opposition-based learning mechanism, the results demonstrate that QRHHO can effectively improve the convergence speed and solution accuracy of the basic HHO and two variants of HHO. At the same time, QRHHO is also better than other swarm-based intelligent algorithms.
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Abbasi A, Firouzi B, Sendur P (2019) On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks. Eng Comput. https://doi.org/10.1007/s00366-019-00892-0
Abd Elaziz M, Oliva D (2018) Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Convers Manag 171:1843–1859. https://doi.org/10.1016/j.enconman.2018.05.062
Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484–500. https://doi.org/10.1016/j.eswa.2017.07.043
da Silveira LA, Soncco-Álvarez JL, de Lima TA, Ayala-Rincón M (2016) Memetic and opposition-based learning genetic algorithms for sorting unsigned genomes by translocations. In: Advances in nature and biologically inspired computing. Springer, pp 73–85. https://doi.org/10.1007/978-3-319-27400-3_7
Das S, Bhattacharya A, Chakraborty AK (2016) Quasi-reflected ions motion optimization algorithm for short-term hydrothermal scheduling. Neural Comput Appl 29:123–149. https://doi.org/10.1007/s00521-016-2529-8
Das S, Bhattacharya A, Chakraborty AK (2017) Solution of short-term hydrothermal scheduling problem using quasi-reflected symbiotic organisms search algorithm considering multi-fuel cost characteristics of thermal generator. Arab J Sci Eng 43:2931–2960. https://doi.org/10.1007/s13369-017-2973-5
Dinkar SK, Deep K (2018) An efficient opposition based Lévy Flight Antlion optimizer for optimization problems. J Comput Sci 29:119–141. https://doi.org/10.1016/j.jocs.2018.10.002
Ergezer M, Simon D, Du D (2009) Oppositional biogeography-based optimization. In: 2009 IEEE international conference on systems, man and cybernetics, 2009. IEEE, pp 1009–1014. https://doi.org/10.1109/ICSMC.2009.5346043
Ewees AA, Abd Elaziz M, Houssein EH (2018a) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172. https://doi.org/10.1016/j.eswa.2018.06.023
Ewees AA, Elaziz MA, Houssein EH (2018b) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:S0957417418303701. https://doi.org/10.1016/j.eswa.2018.06.023
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci 17:4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010
Guha D, Roy PK, Banerjee S (2016) Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm. Eng Sci Technol Int J 19:1693–1713. https://doi.org/10.1016/j.jestch.2016.07.004
Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Future Gener Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015
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 267:66–73. https://doi.org/10.1038/scientificamerican0792-66
Houssein EH, Hosney ME, Oliva D, Mohamed WM, Hassaballah M (2020a) 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
Houssein EH, Saad MR, Hussain K, Zhu W, Shaban H, Hassaballah M (2020b) Optimal sink node placement in large scale wireless sensor networks based on Harris’ hawk optimization algorithm. IEEE Access 8:19381–19397. https://doi.org/10.1109/ACCESS.2020.2968981
Hussain K, Zhu W, Salleh MNM (2019) Long-term memory Harris’ hawk optimization for high dimensional and optimal power flow problems. IEEE Access 7:147596–147616. https://doi.org/10.1109/ACCESS.2019.2946664
Jia H, Lang C, Oliva D, Song W, Peng X (2019) Dynamic Harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens 11:1421. https://doi.org/10.3390/rs11121421
Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput 89:106018. https://doi.org/10.1016/j.asoc.2019.106018
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471. https://doi.org/10.1007/s10898-007-9149-x
Kennedy J, Eberhart R (2002) Particle swarm Optimization. In: Icnn95-international conference on neural networks. https://doi.org/10.1109/icnn.1995.488968
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680. https://doi.org/10.1126/science.220.4598.671
Kurtuluş E, Yildiz A, Sait SM (2020) A novel hybrid Harris hawks- simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails. Mater Test 62:1–15. https://doi.org/10.3139/120.111478
Mirjalili S (2015a) The ant lion optimizer. Adv Eng Softw 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010
Mirjalili S (2015b) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006
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 advances in engineering software. Renew Sustain Energy Rev 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mirjalili S, Mirjalili SM, Hatamlou A (2015) 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
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
Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74. https://doi.org/10.1016/j.knosys.2011.07.001
Qais MH, Hasanien HM, Alghuwainem S (2020) Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization. Energy 195:117040. https://doi.org/10.1016/j.energy.2020.117040
Rahnamayan S, Tizhoosh HR, Salama MM (2007) Quasi-oppositional differential evolution. In: 2007 ieee congress on evolutionary computation, 2007. IEEE, pp 2229–2236. https://doi.org/10.1109/cec.2007.4424748
Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12:64–79. https://doi.org/10.1109/TEVC.2007.894200
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
Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004
Sharma S, Bhattacharjee S, Bhattacharya A (2016) Quasi-oppositional swine influenza model based optimization with quarantine for optimal allocation of DG in radial distribution network. Int J Electr Power Energy Syst 74:348–373. https://doi.org/10.1109/CEC.2007.4424748
Shehabeldeen TA, Elaziz MA, Elsheikh AH, Zhou J (2019) Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with Harris hawks optimizer. J Mater Res Technol 8:5882–5892. https://doi.org/10.1016/j.jmrt.2019.09.060
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. https://doi.org/10.1016/j.asoc.2015.05.054
Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359. https://doi.org/10.1023/A:1008202821328
Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), 2005. IEEE, pp 695–701. https://doi.org/10.1109/cimca.2005.1631345
Too J, Abdullah AR, Mohd Saad N (2019) A new quadratic binary harris hawk optimization for feature selection. Electronics 8:1130. https://doi.org/10.3390/electronics8101130
Truong KH, Nallagownden P, Baharudin Z, Vo DN (2019) A quasi-oppositional-chaotic symbiotic organisms search algorithm for global optimization problems. Appl Soft Comput 77:567–583. https://doi.org/10.1016/j.asoc.2019.01.043
Xin Y, Yong L, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102. https://doi.org/10.1109/4235.771163
Yildiz A, Yildiz BS (2019) The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components. Mater Test 8:60–70. https://doi.org/10.3139/120.111379
Yildiz A, Mirjalili S, Sait SM, Li X (2019) The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations. Mater Test 8:1–15. https://doi.org/10.3139/120.111377
Yıldız AR, Yıldız BS, Sait SM, Bureerat S, Pholdee N (2019) A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems. Mater Test 61:735–743. https://doi.org/10.3139/120.111378
Yildiz BS, Yildiz AR, Pholdee N, Bureerat S, Sait SM, Patel V (2020) The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components. Mater Test 62:5–25. https://doi.org/10.3139/120.111479
Yousri D, Allam D, Eteiba MB (2020) Optimal photovoltaic array reconfiguration for alleviating the partial shading influence based on a modified harris hawks optimizer. Energy Convers Manag 206:112470. https://doi.org/10.1016/j.enconman.2020.112470
Acknowledgements
This paper is supported by National Natural Science Foundation of China (No. 41404008), Open Foundation of Key Laboratory for Digital Land and Resources of Jiangxi Province (No. DLLJ201911) and Guiding Project of Fujian Science and Technology Program (No. 2018Y0021).
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Fan, Q., Chen, Z. & Xia, Z. A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems. Soft Comput 24, 14825–14843 (2020). https://doi.org/10.1007/s00500-020-04834-7
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DOI: https://doi.org/10.1007/s00500-020-04834-7