More Web Proxy on the site http://driver.im/

research-article

MEALPY: : An open-source library for latest meta-heuristic algorithms in Python

Authors:

Nguyen Van Thieu,

Seyedali MirjaliliAuthors Info & Claims

Volume 139, Issue C

https://doi.org/10.1016/j.sysarc.2023.102871

Published: 01 June 2023 Publication History

Abstract

Meta-heuristic algorithms are becoming more prevalent and have been widely applied in various fields. There are numerous reasons for the success of such techniques in both science and industry, including but not limited to simplicity in search/optimization mechanisms, implementation readiness, black-box nature, and ease of use. Although the solutions obtained by such algorithms are not guaranteed to be exactly global optimal, they usually find reasonably good solutions in a reasonable time. Many algorithms have been proposed and developed in the last two decades. However, there is no library implementing meta-heuristic algorithms, which is easy to use and has a vast collection of algorithms. This paper proposes an open-source and cross-platform Python library for nature-inspired optimization algorithms called Mealpy. To propose Mealpy, we analyze the features of existing libraries for meta-heuristic algorithms. After, we propose the designation and the structure of Mealpy and validate it with a case study discussion. Compared with other libraries, our proposed Mealpy has the largest number of classical and state-of-the-art meta-heuristic algorithms, with more than 160 algorithms. Mealpy is an open-source library with well-documented code, has a simple interface, and benefits from minimum dependencies. Mealpy includes a wide range of well-known and recent meta-heuristics algorithms capable of optimizing challenge benchmark functions (e.g. CEC-2017). Mealpy can also be used for practical problems such as optimizing parameters for machine learning models. We invite the research community for widespread evaluations of this comprehensive library as a promising tool for research study and real-world optimization. The source codes, supplementary materials, and guidance is publicly available on GitHub: https://github.com/thieu1995/mealpy.

Graphical abstract

Display Omitted

References

[1]

De Leon-Aldaco S.E., Calleja H., Aguayo Alquicira J., Metaheuristic optimization methods applied to power converters: A review, IEEE Trans. Power Electron. 30 (12) (2015) 6791–6803,. URL: http://ieeexplore.ieee.org/document/7024140/.

[2]

Neapolitan R.E., Naimipour K., Foundations of Algorithms using Java Pseudocode, Jones and Bartlett Publishers, Sudbury, Mass, 2004, OCLC: ocm53138748.

[3]

Mallouk T.E., Divide and conquer, Nature Chem. 5 (5) (2013) 362–363,. URL: http://www.nature.com/articles/nchem.1634.

[4]

de Farias D.P., Van Roy B., The linear programming approach to approximate dynamic programming, Oper. Res. 51 (6) (2003) 850–865,. URL: http://pubsonline.informs.org/doi/abs/10.1287/opre.51.6.850.24925.

Digital Library

[5]

Morrison D.R., Jacobson S.H., Sauppe J.J., Sewell E.C., Branch-and-bound algorithms: A survey of recent advances in searching, branching, and pruning, Discrete Optim. 19 (2016) 79–102,. URL: https://linkinghub.elsevier.com/retrieve/pii/S1572528616000062.

Digital Library

[6]

Civicioglu P., Backtracking search optimization algorithm for numerical optimization problems, Appl. Math. Comput. 219 (15) (2013) 8121–8144,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0096300313001380.

Digital Library

[7]

Beheshti Z., Shamsuddin S.M.H., A review of population-based meta-heuristic algorithms, Int. J. Adv. Soft Comput. Appl. 5 (1) (2013) 1–35.

[8]

Talatahari S., Bayzidi H., Saraee M., Social network search for global optimization, IEEE Access 9 (2021) 92815–92863,. URL: https://ieeexplore.ieee.org/document/9462076/.

[9]

Ahmed A.N., Van Lam T., Hung N.D., Van Thieu N., Kisi O., El-Shafie A., A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem, Appl. Soft Comput. 105 (2021),. URL: https://linkinghub.elsevier.com/retrieve/pii/S1568494621002052.

Digital Library

[10]

van Laarhoven P.J.M., Aarts E.H.L., Simulated Annealing: Theory and Applications, Springer Netherlands, Dordrecht, 1987,. URL: http://link.springer.com/10.1007/978-94-015-7744-1.

[11]

Whitley D., A genetic algorithm tutorial, Stat. Comput. 4 (2) (1994) 65–85.

[12]

Mallipeddi R., Suganthan P.N., Pan Q.-K., Tasgetiren M.F., Differential evolution algorithm with ensemble of parameters and mutation strategies, Appl. Soft Comput. 11 (2) (2011) 1679–1696,.

Digital Library

[13]

Kennedy J., Eberhart R., Particle swarm optimization, in: Proceedings of ICNN’95 - International Conference on Neural Networks, Vol. 4, IEEE, Perth, WA, Australia, 1995, pp. 1942–1948,. URL: http://ieeexplore.ieee.org/document/488968/.

[14]

Socha K., Dorigo M., Ant colony optimization for continuous domains, European J. Oper. Res. 185 (3) (2008) 1155–1173,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0377221706006333.

[15]

Mirjalili S., Lewis A., The whale optimization algorithm, Adv. Eng. Softw. 95 (2016) 51–67,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0965997816300163.

Digital Library

[16]

Ho Y., Pepyne D., Simple explanation of the no-free-lunch theorem and its implications, J. Optim. Theory Appl. 115 (3) (2002) 549–570,. URL: http://link.springer.com/10.1023/A:1021251113462.

Digital Library

[17]

Faris H., Aljarah I., Mirjalili S., Castillo P.A., Guervós J.J.M., EvoloPy: An open-source nature-inspired optimization framework in python., Int. J. Child-Comput. Interact. (ECTA) 1 (2016) 171–177. URL: https://github.com/7ossam81/EvoloPy.

[18]

Fortin F.-A., De Rainville F.-M., Gardner M.-A., Parizeau M., Gagné C., DEAP: Evolutionary algorithms made easy, J. Mach. Learn. Res. 13 (2012) 2171–2175. URL: https://github.com/DEAP/deap.

Digital Library

[19]

Salcedo-Sanz S., Del Ser J., Landa-Torres I., Gil-López S., Portilla-Figueras J., The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems, Sci. World J. 2014 (2014),.

[20]

Nguyen T., Nguyen T., Nguyen B.M., Nguyen G., Efficient time-series forecasting using neural network and opposition-based coral reefs optimization, Int. J. Comput. Intell. Syst. 12 (2) (2019) 1144,.

[21]

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

Digital Library

[22]

Qin A.K., Suganthan P.N., Self-adaptive differential evolution algorithm for numerical optimization, in: 2005 IEEE Congress on Evolutionary Computation, Vol. 2, IEEE, 2005, pp. 1785–1791,.

[23]

Tanabe R., Fukunaga A., Success-history based parameter adaptation for differential evolution, in: 2013 IEEE Congress on Evolutionary Computation, IEEE, 2013, pp. 71–78,.

[24]

Tanabe R., Fukunaga A.S., Improving the search performance of SHADE using linear population size reduction, in: 2014 IEEE Congress on Evolutionary Computation, CEC, IEEE, 2014, pp. 1658–1665,.

[25]

Teo J., Exploring dynamic self-adaptive populations in differential evolution, Soft Comput. 10 (8) (2006) 673–686,.

Digital Library

[26]

Beyer H.-G., Schwefel H.-P., Evolution strategies–a comprehensive introduction, Nat. Comput. 1 (1) (2002) 3–52,.

Digital Library

[27]

Salari E., Competitive learning vector quantization with evolution strategies for image compression, Opt. Eng. 44 (2) (2005),. URL: http://opticalengineering.spiedigitallibrary.org/article.aspx?doi=10.1117/1.1839892.

[28]

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

Digital Library

[29]

Lee C.-Y., Yao X., Evolutionary algorithms with adaptive Levy mutations, in: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Vol. 1, IEEE, Seoul, South Korea, 2001, pp. 568–575,. URL: http://ieeexplore.ieee.org/document/934442/.

[30]

Yang X.-S., Flower pollination algorithm for global optimization, in: International Conference on Unconventional Computing and Natural Computation, Springer, 2012, pp. 240–249,.

[31]

Moscato P., et al., On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms, Citeseer, 1989, p. 1989.

[32]

Abdollahzadeh B., Gharehchopogh F.S., Mirjalili S., African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Comput. Ind. Eng. 158 (2021),.

Digital Library

[33]

Mirjalili S., The ant lion optimizer, Adv. Eng. Softw. 83 (2015) 80–98,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0965997815000113.

Digital Library

[34]

Abualigah L., Yousri D., Abd Elaziz M., Ewees A.A., Al-qaness M.A., Gandomi A.H., Aquila Optimizer: A novel meta-heuristic optimization algorithm, Comput. Ind. Eng. 157 (2021),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0360835221001546.

[35]

Karaboga D., Basturk B., On the performance of artificial bee colony (ABC) algorithm, Appl. Soft Comput. 8 (1) (2008) 687–697,. URL: https://linkinghub.elsevier.com/retrieve/pii/S1568494607000531.

Digital Library

[36]

Abdollahzadeh B., Soleimanian Gharehchopogh F., Mirjalili S., Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems, Int. J. Intell. Syst. 36 (10) (2021) 5887–5958,.

Digital Library

[37]

Wang L., Cao Q., Zhang Z., Mirjalili S., Zhao W., Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems, Eng. Appl. Artif. Intell. 114 (2022),.

[38]

Passino K.M., Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Syst. Mag. 22 (3) (2002) 52–67,. URL: https://ieeexplore.ieee.org/document/1004010/.

[39]

Nguyen T., Nguyen B.M., Nguyen G., Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization, in: Gopal T., Watada J. (Eds.), Theory and Applications of Models of Computation, vol. 11436, Springer International Publishing, Cham, 2019, pp. 501–517,. URL: http://link.springer.com/10.1007/978-3-030-14812-6_31.

Digital Library

[40]

Alsattar H.A., Zaidan A.A., Zaidan B.B., Novel meta-heuristic bald eagle search optimisation algorithm, Artif. Intell. Rev. 53 (3) (2020) 2237–2264,. URL: http://link.springer.com/10.1007/s10462-019-09732-5.

[41]

Yang X.-S., A new metaheuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Vol. 284, Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, pp. 65–74,. URL: http://link.springer.com/10.1007/978-3-642-12538-6_6.

[42]

Wang X., Wang W., Wang Y., An adaptive bat algorithm, in: Intelligent Computing Theories and Technology, vol. 7996, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 216–223,. URL: http://link.springer.com/10.1007/978-3-642-39482-9_25.

Digital Library

[43]

Jaddi N.S., Abdullah S., Hamdan A.R., Optimization of neural network model using modified bat-inspired algorithm, Appl. Soft Comput. 37 (2015) 71–86,. URL: https://linkinghub.elsevier.com/retrieve/pii/S1568494615004950.

Digital Library

[44]

Pham D.T., Ghanbarzadeh A., Koç E., Otri S., Rahim S., Zaidi M., The Bees algorithm — A novel tool for complex optimisation problems, in: Intelligent Production Machines and Systems, Elsevier Science Ltd, Oxford, 2006, pp. 454–459,. URL: https://www.sciencedirect.com/science/article/pii/B978008045157250081X.

[45]

Pham D.T., Castellani M., A comparative study of the Bees Algorithm as a tool for function optimisation, Chen J. (Ed.), Cogent Eng. 2 (1) (2015),. URL: https://www.tandfonline.com/doi/full/10.1080/23311916.2015.1091540.

[46]

Meng X.-B., Gao X., Lu L., Liu Y., Zhang H., A new bio-inspired optimisation algorithm: Bird Swarm Algorithm, J. Exp. Theor. Artif. Intell. 28 (4) (2016) 673–687,. URL: http://www.tandfonline.com/doi/full/10.1080/0952813X.2015.1042530.

[47]

Chu S.-C., Tsai P.-w., Pan J.-S., Cat swarm optimization, in: Yang Q., Webb G. (Eds.), PRICAI 2006: Trends in Artificial Intelligence, Springer, Berlin, Heidelberg, 2006, pp. 854–858,.

[48]

Pierezan J., Dos Santos Coelho L., Coyote optimization algorithm: A new metaheuristic for global optimization problems, in: 2018 IEEE Congress on Evolutionary Computation, CEC, 2018, pp. 1–8,.

Digital Library

[49]

Yang X.-S., Deb S., Cuckoo search via Lévy flights, in: 2009 World Congress on Nature & Biologically Inspired Computing, NaBIC, 2009, pp. 210–214,.

[50]

Mirjalili S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Comput. Appl. 27 (4) (2016) 1053–1073,. URL: http://link.springer.com/10.1007/s00521-015-1920-1.

Digital Library

[51]

Agushaka J.O., Ezugwu A.E., Abualigah L., Dwarf mongoose optimization algorithm, Comput. Methods Appl. Mech. Engrg. 391 (2022),.

[52]

Wang G.-G., Deb S., Coelho L.d.S., Elephant herding optimization, in: 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI, IEEE, Bali, Indonesia, 2015, pp. 1–5,. URL: http://ieeexplore.ieee.org/document/7383528/.

Digital Library

[53]

Gandomi A.H., Yang X.-S., Alavi A.H., Mixed variable structural optimization using Firefly Algorithm, Comput. Struct. 89 (23–24) (2011) 2325–2336,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0045794911002185.

Digital Library

[54]

Tan Y., Zhu Y., Fireworks Algorithm for Optimization, in: Advances in Swarm Intelligence, Vol. 6145, Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, pp. 355–364,. URL: http://link.springer.com/10.1007/978-3-642-13495-1_44.

Digital Library

[55]

Pan W.-T., A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example, Knowl.-Based Syst. 26 (2012) 69–74,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0950705111001365.

Digital Library

[56]

Fan Y., Wang P., Heidari A.A., Wang M., Zhao X., Chen H., Li C., Boosted hunting-based fruit fly optimization and advances in real-world problems, Expert Syst. Appl. 159 (2020),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0957417420303262.

[57]

Saremi S., Mirjalili S., Lewis A., Grasshopper Optimisation Algorithm: Theory and application, Adv. Eng. Softw. 105 (2017) 30–47,. URL: https://www.sciencedirect.com/science/article/pii/S0965997816305646.

Digital Library

[58]

Mirjalili S., Mirjalili S.M., Lewis A., Grey Wolf Optimizer, Adv. Eng. Softw. 69 (2014) 46–61,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0965997813001853.

Digital Library

[59]

Gupta S., Deep K., A novel Random Walk Grey Wolf Optimizer, Swarm Evol. Comput. 44 (2019) 101–112,. URL: https://linkinghub.elsevier.com/retrieve/pii/S2210650217305333.

[60]

Obadina O.O., Thaha M.A., Althoefer K., Shaheed M.H., Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm, J. Vib. Control (2021),.

[61]

Heidari A.A., Mirjalili S., Faris H., Aljarah I., Mafarja M., Chen H., Harris hawks optimization: Algorithm and applications, Future Gener. Comput. Syst. 97 (2019) 849–872,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0167739X18313530.

Digital Library

[62]

Hashim F.A., Houssein E.H., Hussain K., Mabrouk M.S., Al-Atabany W., Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems, Math. Comput. Simulation 192 (2022) 84–110,.

[63]

Yang Y., Chen H., Heidari A.A., Gandomi A.H., Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts, Expert Syst. Appl. 177 (2021),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0957417421003055.

Digital Library

[64]

Venkata Rao R., Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems, Int. J. Ind. Eng. Comput. (2016) 19–34,. URL: http://www.growingscience.com/ijiec/Vol7/IJIEC_2015_32.pdf.

[65]

Iacca G., dos Santos Junior V.C., Veloso de Melo V., An improved Jaya optimization algorithm with Lévy flight, Expert Syst. Appl. 165 (2021),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0957417420306989.

[66]

Zhao W., Zhang Z., Wang L., Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications, Eng. Appl. Artif. Intell. 87 (2020),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0952197619302593.

Digital Library

[67]

Faramarzi A., Heidarinejad M., Mirjalili S., Gandomi A.H., Marine predators algorithm: A nature-inspired metaheuristic, Expert Syst. Appl. 152 (2020),.

[68]

Mirjalili S., Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl.-Based Syst. 89 (2015) 228–249,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0950705115002580.

Digital Library

[69]

Wang G.-G., Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems, Memet. Comput. 10 (2) (2018) 151–164,. URL: http://link.springer.com/10.1007/s12293-016-0212-3.

[70]

Salgotra R., Singh U., The naked mole-rat algorithm, Neural Comput. Appl. 31 (12) (2019) 8837–8857,. URL: http://link.springer.com/10.1007/s00521-019-04464-7.

[71]

Ghasemi M., Akbari E., Rahimnejad A., Razavi S.E., Ghavidel S., Li L., Phasor particle swarm optimization: a simple and efficient variant of PSO, Soft Comput. 23 (19) (2019) 9701–9718,. URL: http://link.springer.com/10.1007/s00500-018-3536-8.

Digital Library

[72]

Ghasemi M., Aghaei J., Hadipour M., New self-organising hierarchical PSO with jumping time-varying acceleration coefficients, Electron. Lett. 53 (20) (2017) 1360–1362,. URL: https://onlinelibrary.wiley.com/doi/10.1049/el.2017.2112.

[73]

Liu B., Wang L., Jin Y.-H., Tang F., Huang D.-X., Improved particle swarm optimization combined with chaos, Chaos Solitons Fractals 25 (5) (2005) 1261–1271,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0960077905000330.

[74]

Liang J., Qin A., Suganthan P., Baskar S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput. 10 (3) (2006) 281–295,. URL: http://ieeexplore.ieee.org/document/1637688/.

Digital Library

[75]

Yapici H., Cetinkaya N., A new meta-heuristic optimizer: Pathfinder algorithm, Appl. Soft Comput. 78 (2019) 545–568,. URL: https://linkinghub.elsevier.com/retrieve/pii/S1568494619301309.

Digital Library

[76]

Shadravan S., Naji H., Bardsiri V., The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems, Eng. Appl. Artif. Intell. 80 (2019) 20–34,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0952197619300016.

[77]

Li L.-L., Shen Q., Tseng M.-L., Luo S., Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm, J. Clean. Prod. 316 (2021),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0959652621025336.

[78]

Mirjalili S., Gandomi A.H., Mirjalili S.Z., Saremi S., Faris H., Mirjalili S.M., Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Adv. Eng. Softw. 114 (2017) 163–191,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0965997816307736.

Digital Library

[79]

Seyyedabbasi A., Kiani F., Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems, Eng. Comput. (2022) 1–25,.

[80]

Masadeh R., A. B., Sharieh A., Sea Lion Optimization Algorithm, Int. J. Adv. Comput. Sci. Appl. 10 (5) (2019),. URL: http://thesai.org/Publications/ViewPaper?Volume=10&Issue=5&Code=IJACSA&SerialNo=48.

[81]

Masadeh R., Alsharman N., Sharieh A., Mahafzah B.A., Abdulrahman A., Task scheduling on cloud computing based on sea lion optimization algorithm, Int. J. Web Inf. Syst. 17 (2) (2021) 99–116,. URL: https://www.emerald.com/insight/content/doi/10.1108/IJWIS-11-2020-0071/full/html.

[82]

Nguyen B.M., Tran T., Nguyen T., Nguyen G., An improved sea lion optimization for workload elasticity prediction with neural networks, Int. J. Comput. Intell. Syst. 15 (1) (2022) 1–26,.

[83]

Yu J.J., Li V.O., A social spider algorithm for global optimization, Appl. Soft Comput. 30 (2015) 614–627,. URL: https://linkinghub.elsevier.com/retrieve/pii/S1568494615001052.

Digital Library

[84]

Luque-Chang A., Cuevas E., Fausto F., Zaldívar D., Pérez M., Social spider optimization algorithm: Modifications, applications, and perspectives, Math. Probl. Eng. 2018 (2018),. URL: https://www.hindawi.com/journals/mpe/2018/6843923/.

[85]

Xue J., Shen B., A novel swarm intelligence optimization approach: sparrow search algorithm, Syst. Sci. Control Eng. 8 (1) (2020) 22–34,. URL: https://www.tandfonline.com/doi/full/10.1080/21642583.2019.1708830.

[86]

Dhiman G., Kumar V., Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications, Adv. Eng. Softw. 114 (2017) 48–70,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0965997816305567.

[87]

Bakhshipour M., Jabbari Ghadi M., Namdari F., Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach, Appl. Soft Comput. 57 (2017) 708–726,. URL: https://linkinghub.elsevier.com/retrieve/pii/S1568494617301072.

Digital Library

[88]

Xie L., Han T., Zhou H., Zhang Z.-R., Han B., Tang A., Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization, Comput. Intell. Neurosci. 2021 (2021),.

Digital Library

[89]

Tang C., Sun W., Wu W., Xue M., A hybrid improved whale optimization algorithm, in: 2019 IEEE 15th International Conference on Control and Automation, ICCA, IEEE, Edinburgh, United Kingdom, 2019, pp. 362–367,. URL: https://ieeexplore.ieee.org/document/8900003/.

[90]

Hashim F.A., Hussain K., Houssein E.H., Mabrouk M.S., Al-Atabany W., Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems, Appl. Intell. 51 (3) (2021) 1531–1551,. URL: https://link.springer.com/10.1007/s10489-020-01893-z.

Digital Library

[91]

Zhao W., Wang L., Zhang Z., Atom search optimization and its application to solve a hydrogeologic parameter estimation problem, Knowl.-Based Syst. 163 (2019) 283–304,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0950705118304271.

[92]

Abedinpourshotorban H., Mariyam Shamsuddin S., Beheshti Z., Jawawi D.N., Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm, Swarm Evol. Comput. 26 (2016) 8–22,. URL: https://linkinghub.elsevier.com/retrieve/pii/S2210650215000528.

[93]

Faramarzi A., Heidarinejad M., Stephens B., Mirjalili S., Equilibrium optimizer: A novel optimization algorithm, Knowl.-Based Syst. 191 (2020),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0950705119305295.

Digital Library

[94]

Gupta S., Deep K., Mirjalili S., An efficient equilibrium optimizer with mutation strategy for numerical optimization, Appl. Soft Comput. 96 (2020),. URL: https://linkinghub.elsevier.com/retrieve/pii/S1568494620304816.

Digital Library

[95]

Wunnava A., Naik M.K., Panda R., Jena B., Abraham A., A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer, Eng. Appl. Artif. Intell. 94 (2020),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0952197620302037.

[96]

Hashim F.A., Houssein E.H., Mabrouk M.S., Al-Atabany W., Mirjalili S., Henry gas solubility optimization: A novel physics-based algorithm, Future Gener. Comput. Syst. 101 (2019) 646–667,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0167739X19306557.

Digital Library

[97]

Mirjalili S., Mirjalili S.M., Hatamlou A., Multi-Verse Optimizer: a nature-inspired algorithm for global optimization, Neural Comput. Appl. 27 (2) (2016) 495–513,. URL: https://doi.org/10.1007/s00521-015-1870-7.

Digital Library

[98]

Wei Z., Huang C., Wang X., Han T., Li Y., Nuclear reaction optimization: A novel and powerful physics-based algorithm for global optimization, IEEE Access 7 (2019) 66084–66109,.

[99]

Kaveh A., Tug of war optimization, in: Advances in Metaheuristic Algorithms for Optimal Design of Structures, Springer International Publishing, Cham, 2017, pp. 451–487,. URL: http://link.springer.com/10.1007/978-3-319-46173-1_15.

[100]

Kaveh A., Almasi P., Khodagholi A., Optimum design of castellated beams using four recently developed meta-heuristic algorithms, Iran. J. Sci. Technol. Trans. Civil Eng. (2022),. URL: https://link.springer.com/10.1007/s40996-022-00884-z.

[101]

Nguyen T., Hoang B., Nguyen G., Nguyen B.M., A new workload prediction model using extreme learning machine and enhanced tug of war optimization, Procedia Comput. Sci. 170 (2020) 362–369,. URL: https://linkinghub.elsevier.com/retrieve/pii/S1877050920305007.

[102]

Bayraktar Z., Komurcu M., Bossard J.A., Werner D.H., The wind driven optimization technique and its application in electromagnetics, IEEE Trans. Antennas and Propagation 61 (5) (2013) 2745–2757,. URL: http://ieeexplore.ieee.org/document/6407788/.

[103]

Rahkar Farshi T., Battle royale optimization algorithm, Neural Comput. Appl. 33 (4) (2021) 1139–1157,.

Digital Library

[104]

Shi Y., Brain storm optimization algorithm, in: International Conference in Swarm Intelligence, Springer, 2011, pp. 303–309,.

[105]

El-Abd M., Global-best brain storm optimization algorithm, Swarm Evol. Comput. 37 (2017) 27–44,. URL: https://linkinghub.elsevier.com/retrieve/pii/S2210650216301766.

[106]

Al-Betar M.A., Alyasseri Z.A.A., Awadallah M.A., Abu Doush I., Coronavirus herd immunity optimizer (CHIO), Neural Comput. Appl. 33 (10) (2021) 5011–5042,.

Digital Library

[107]

Chen B., Zhao L., Lu J.H., Wind power forecast using RBF network and culture algorithm, in: 2009 International Conference on Sustainable Power Generation and Supply, IEEE, 2009, pp. 1–6,.

[108]

Chou J.-S., Nguyen N.-M., FBI inspired meta-optimization, Appl. Soft Comput. 93 (2020),.

[109]

Fathy A., Rezk H., Alanazi T.M., Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell, IEEE Access 9 (2021) 18974–18992,. URL: https://ieeexplore.ieee.org/document/9335569/.

[110]

Mohamed A.W., Hadi A.A., Mohamed A.K., Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm, Int. J. Mach. Learn. Cybern. 11 (7) (2020) 1501–1529,.

[111]

Mohamed A.W., Hadi A.A., Mohamed A.K., Awad N.H., Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems, in: 2020 IEEE Congress on Evolutionary Computation, CEC, IEEE, Glasgow, United Kingdom, 2020, pp. 1–8,. URL: https://ieeexplore.ieee.org/document/9185901/.

Digital Library

[112]

Atashpaz-Gargari E., Lucas C., Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, in: 2007 IEEE Congress on Evolutionary Computation, Ieee, 2007, pp. 4661–4667,.

[113]

Khatri A., Gaba A., Rana K., Kumar V., A novel life choice-based optimizer, Soft Comput. 24 (12) (2020) 9121–9141,.

Digital Library

[114]

Zhang J., Xiao M., Gao L., Pan Q., Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems, Appl. Math. Model. 63 (2018) 464–490,.

[115]

Zheng X., Nguyen H., A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm, Chemosphere 287 (2022),.

[116]

Abderazek H., Hamza F., Yildiz A.R., Gao L., Sait S.M., A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system, Mater. Test. 63 (5) (2021) 442–447,.

[117]

Nguyen B.M., Hoang B., Nguyen T., Nguyen G., nQSV-Net: a novel queuing search variant for global space search and workload modeling, J. Ambient Intell. Humaniz. Comput. 12 (1) (2021) 27–46,.

[118]

Shabani A., Asgarian B., Gharebaghi S.A., Salido M.A., Giret A., A new optimization algorithm based on search and rescue operations, Math. Probl. Eng. 2019 (2019) 1–23,. URL: https://www.hindawi.com/journals/mpe/2019/2482543/.

[119]

Tharwat A., Gabel T., Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm, Neural Comput. Appl. 32 (11) (2020) 6925–6938,. URL: http://link.springer.com/10.1007/s00521-019-04159-z.

Digital Library

[120]

Das B., Mukherjee V., Das D., Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems, Adv. Eng. Softw. 146 (2020),.

[121]

Rao R., Savsani V., Vakharia D., Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput. Aided Des. 43 (3) (2011) 303–315,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0010448510002484.

Digital Library

[122]

Rao R.V., Patel V., An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems, Int. J. Ind. Eng. Comput. 3 (4) (2012) 535–560,. URL: http://www.growingscience.com/ijiec/Vol3/IJIEC_2012_37.pdf.

[123]

Rao R.V., Patel V., An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems, Sci. Iran. (2012),. URL: https://linkinghub.elsevier.com/retrieve/pii/S1026309812002672.

[124]

Sulaiman M.H., Mustaffa Z., Saari M.M., Daniyal H., Barnacles mating optimizer: a new bio-inspired algorithm for solving engineering optimization problems, Eng. Appl. Artif. Intell. 87 (2020),.

Digital Library

[125]

Simon D., Biogeography-based optimization, IEEE Trans. Evol. Comput. 12 (6) (2008) 702–713,.

Digital Library

[126]

Wang G.-G., Deb S., Coelho L.D.S., Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems, Int. J. Bio-Inspired Comput. 12 (1) (2018) 1–22,.

[127]

Mehrabian A.R., Lucas C., A novel numerical optimization algorithm inspired from weed colonization, Ecol. Inform. 1 (4) (2006) 355–366,.

[128]

Moosavi S.H.S., Bardsiri V.K., Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation, Eng. Appl. Artif. Intell. 60 (2017) 1–15,.

Digital Library

[129]

Dhiman G., Kumar V., Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowl.-Based Syst. 165 (2019) 169–196,.

[130]

Li S., Chen H., Wang M., Heidari A.A., Mirjalili S., Slime mould algorithm: A new method for stochastic optimization, Future Gener. Comput. Syst. 111 (2020) 300–323,.

[131]

Cheng M.-Y., Prayogo D., Symbiotic organisms search: a new metaheuristic optimization algorithm, Comput. Struct. 139 (2014) 98–112,.

[132]

Kaur S., Awasthi L.K., Sangal A., Dhiman G., Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Eng. Appl. Artif. Intell. 90 (2020),.

Digital Library

[133]

Li M.D., Zhao H., Weng X.W., Han T., A novel nature-inspired algorithm for optimization: Virus colony search, Adv. Eng. Softw. 92 (2016) 65–88,.

Digital Library

[134]

Amali D., Dinakaran M., Wildebeest herd optimization: a new global optimization algorithm inspired by wildebeest herding behaviour, J. Intell. Fuzzy Systems 37 (6) (2019) 8063–8076,.

[135]

Villaseñor C., Arana-Daniel N., Alanis A.Y., López-Franco C., Hernandez-Vargas E.A., Germinal center optimization algorithm, Int. J. Comput. Intell. Syst. 12 (1) (2018) 13,. URL: https://www.atlantis-press.com/article/25905179.

[136]

Eskandar H., Sadollah A., Bahreininejad A., Hamdi M., Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems, Comput. Struct. 110–111 (2012) 151–166,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0045794912001770.

Digital Library

[137]

Zhao W., Wang L., Zhang Z., Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm, Neural Comput. Appl. 32 (13) (2020) 9383–9425,. URL: http://link.springer.com/10.1007/s00521-019-04452-x.

[138]

Eid A., Kamel S., Korashy A., Khurshaid T., An enhanced artificial ecosystem-based optimization for optimal allocation of multiple distributed generations, IEEE Access 8 (2020) 178493–178513,. URL: https://ieeexplore.ieee.org/document/9208695/.

[139]

S. Menesy A., Sultan H.M., Korashy A., Banakhr F.A., G. Ashmawy M., Kamel S., Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm, IEEE Access 8 (2020) 31892–31909,. URL: https://ieeexplore.ieee.org/document/8995515/.

[140]

Rizk-Allah R.M., El-Fergany A.A., Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model, Int. J. Hydrogen Energy 46 (75) (2021) 37612–37627,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0360319920324472.

[141]

Van Thieu N., Barma S.D., Van Lam T., Kisi O., Mahesha A., Groundwater level modeling using Augmented Artificial Ecosystem Optimization, J. Hydrol. (2022),. URL: https://doi.org/10.1016/j.jhydrol.2022.129034.

[142]

Abualigah L., Diabat A., Mirjalili S., Abd Elaziz M., Gandomi A.H., The arithmetic optimization algorithm, Comput. Methods Appl. Mech. Engrg. 376 (2021),. URL: https://linkinghub.elsevier.com/retrieve/pii/S0045782520307945.

[143]

Talatahari S., Azizi M., Chaos game optimization: a novel metaheuristic algorithm, Artif. Intell. Rev. 54 (2) (2021) 917–1004,. URL: https://link.springer.com/10.1007/s10462-020-09867-w.

Digital Library

[144]

Qais M.H., Hasanien H.M., Turky R.A., Alghuwainem S., Tostado-Véliz M., Jurado F., Circle search algorithm: A geometry-based metaheuristic optimization algorithm, Mathematics 10 (10) (2022) 1626,.

[145]

Rubinstein R., The cross-entropy method for combinatorial and continuous optimization, Methodol. Comput. Appl. Probab. 1 (2) (1999) 127–190,.

Digital Library

[146]

Ahmadianfar I., Bozorg-Haddad O., Chu X., Gradient-based optimizer: A new metaheuristic optimization algorithm, Inform. Sci. 540 (2020) 131–159,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0020025520306241.

[147]

Prügel-Bennett A., When a genetic algorithm outperforms hill-climbing, Theoret. Comput. Sci. 320 (1) (2004) 135–153,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0304397504001999.

Digital Library

[148]

Shaqfa M., Beyer K., Pareto-like sequential sampling heuristic for global optimisation, Soft Comput. 25 (14) (2021) 9077–9096,. URL: https://link.springer.com/10.1007/s00500-021-05853-8.

Digital Library

[149]

Ahmadianfar I., Heidari A.A., Gandomi A.H., Chu X., Chen H., RUN beyond the metaphor: An efficient optimization algorithm based on runge kutta method, Expert Syst. Appl. 181 (2021),.

[150]

Mirjalili S., SCA: A Sine cosine algorithm for solving optimization problems, Knowl.-Based Syst. 96 (2016) 120–133,. URL: https://linkinghub.elsevier.com/retrieve/pii/S0950705115005043.

Digital Library

[151]

Ahmadianfar I., Heidari A.A., Noshadian S., Chen H., Gandomi A.H., INFO: An efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl. 195 (2022),.

Digital Library

[152]

Hussain K., Salleh M.N.M., Cheng S., Shi Y., On the exploration and exploitation in popular swarm-based metaheuristic algorithms, Neural Comput. Appl. 31 (11) (2019) 7665–7683,. URL: http://link.springer.com/10.1007/s00521-018-3592-0.

Digital Library

[153]

Yang X.-S., Nature-Inspired Optimization Algorithms, first ed., Elsevier, Amsterdam, Boston, 2014,.

[154]

P.-A. Simionescu, D.G. Beale, New concepts in graphic visualization of objective functions, in: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 36223, 2002, pp. 891–897, https://doi.org/10.1115/DETC2002/DAC-34129.

[155]

Lim W.J., Jambek A.B., Neoh S.C., Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA), Soft Comput. 19 (12) (2015) 3571–3580,.

Digital Library

[156]

G. Wu, R. Mallipeddi, P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization, National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report, 2017.

[157]

Wolberg W.H., Street W.N., Mangasarian O.L., Image analysis and machine learning applied to breast cancer diagnosis and prognosis, Anal. Quant. Cytol. Histol. 17 (2) (1995) 77–87.

Cited By

Zhong RZhang CYu J(2025)Improved snow ablation optimization for multilevel threshold image segmentationCluster Computing10.1007/s10586-024-04785-w28:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10586-024-04785-w
Vermetten DDoerr CWang HKononova ABäck TLi XHandl J(2024)Large-Scale Benchmarking of Metaphor-Based Optimization HeuristicsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654122(41-49)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654122
Van Thieu NNguyen NHeidari A(2024)Feature selection using metaheuristics made easyFuture Generation Computer Systems10.1016/j.future.2024.06.006160:C(340-358)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.future.2024.06.006
Show More Cited By

Recommendations

Crossover-based artificial bee colony algorithm for constrained optimization problems

Artificial bee colony (ABC) algorithm represents one of the most-studied swarm intelligence algorithms. Since the original ABC has been found to be very effective, today there are a lot of improved variants of ABC algorithm used to solve a wide range of ...
Applications of nature-inspired meta-heuristic algorithms: a survey

Nature-inspired meta-heuristic algorithms (NIMAs) combine the properties of nature-inspired and mete-heuristic algorithms. Evolutionary inspired and swarm-inspired algorithms are the most researched techniques for the developments of new nature-inspired ...
Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization

To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Systems Architecture: the EUROMICRO Journal

Journal of Systems Architecture: the EUROMICRO Journal Volume 139, Issue C

Jun 2023

129 pages

ISSN:1383-7621

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 June 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhong RZhang CYu J(2025)Improved snow ablation optimization for multilevel threshold image segmentationCluster Computing10.1007/s10586-024-04785-w28:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s10586-024-04785-w
Vermetten DDoerr CWang HKononova ABäck TLi XHandl J(2024)Large-Scale Benchmarking of Metaphor-Based Optimization HeuristicsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654122(41-49)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654122
Van Thieu NNguyen NHeidari A(2024)Feature selection using metaheuristics made easyFuture Generation Computer Systems10.1016/j.future.2024.06.006160:C(340-358)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.future.2024.06.006
Thaher TSheta AAwad MAldasht M(2024)Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121712238:PAOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121712
da Silva LSeman LCamponogara EMariani Vdos Santos Coelho L(2024)Bilinear optimization of protein structure predictionComputers in Biology and Medicine10.1016/j.compbiomed.2024.108558176:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108558
Sulaman MGolabi MEssaid MLepagnot JBrévilliers MIdoumghar L(2024)Surrogate-assisted metaheuristics for the facility location problem with distributed demands on network edgesComputers and Industrial Engineering10.1016/j.cie.2024.109931188:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.109931
Rojas MOlivera AVidal P(2024)A genetic operators-based Ant Lion Optimiser for training a medical multi-layer perceptron▪Applied Soft Computing10.1016/j.asoc.2023.111192151:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111192
Zhong RZhang EMunetomo M(2024)Evolutionary multi-mode slime mold optimization: a hyper-heuristic algorithm inspired by slime mold foraging behaviorsThe Journal of Supercomputing10.1007/s11227-024-05909-080:9(12186-12217)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-05909-0
Zhong RYu J(2024)A novel evolutionary status guided hyper-heuristic algorithm for continuous optimizationCluster Computing10.1007/s10586-024-04593-227:9(12209-12238)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04593-2
Zhong RFan QZhang CYu J(2024)Hybrid remora crayfish optimization for engineering and wireless sensor network coverage optimizationCluster Computing10.1007/s10586-024-04508-127:7(10141-10168)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10586-024-04508-1
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents