A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems Based on Technical and Vocational Education and Training

. 2023 Oct 23 ; 8 (6) : . [epub] 20231023

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid37887639

Grantová podpora
Specific Research Project No 2126, 2023 Faculty of Education, University of Hradec Králove

In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational education and training schools. The theory of TVETBO is expressed and mathematically modeled in three phases: (i) theory education, (ii) practical education, and (iii) individual skills development. The performance of TVETBO when solving optimization problems is evaluated on the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that TVETBO, with its high abilities to explore, exploit, and create a balance between exploration and exploitation during the search process, is able to provide effective solutions for the benchmark functions. The results obtained from TVETBO are compared with the performances of twelve well-known metaheuristic algorithms. A comparison of the simulation results and statistical analysis shows that the proposed TVETBO approach provides better results in most of the benchmark functions and provides a superior performance in competition with competitor algorithms. Furthermore, in order to measure the effectiveness of the proposed approach in dealing with real-world applications, TVETBO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite. The simulation results show that TVETBO provides an effective and superior performance when solving constrained optimization problems of real-world applications compared to competitor algorithms.

Zobrazit více v PubMed

Zhao S., Zhang T., Ma S., Chen M. Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Eng. Appl. Artif. Intell. 2022;114:105075. doi: 10.1016/j.engappai.2022.105075. DOI

Sergeyev Y.D., Kvasov D., Mukhametzhanov M. On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget. Sci. Rep. 2018;8:453. PubMed PMC

Liberti L., Kucherenko S. Comparison of deterministic and stochastic approaches to global optimization. Int. Trans. Oper. Res. 2005;12:263–285. doi: 10.1111/j.1475-3995.2005.00503.x. DOI

Koc I., Atay Y., Babaoglu I. Discrete tree seed algorithm for urban land readjustment. Eng. Appl. Artif. Intell. 2022;112:104783. doi: 10.1016/j.engappai.2022.104783. DOI

Dehghani M., Trojovská E., Trojovský P. A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process. Sci. Rep. 2022;12:9924. doi: 10.1038/s41598-022-14225-7. PubMed DOI PMC

Zeidabadi F.-A., Dehghani M., Trojovský P., Hubálovský Š., Leiva V., Dhiman G. Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems. Comput. Mater. Contin. 2022;72:399–416. doi: 10.32604/cmc.2022.024736. DOI

de Armas J., Lalla-Ruiz E., Tilahun S.L., Voß S. Similarity in metaheuristics: A gentle step towards a comparison methodology. Nat. Comput. 2022;21:265–287. doi: 10.1007/s11047-020-09837-9. DOI

Dehghani M., Montazeri Z., Dehghani A., Malik O.P., Morales-Menendez R., Dhiman G., Nouri N., Ehsanifar A., Guerrero J.M., Ramirez-Mendoza R.A. Binary spring search algorithm for solving various optimization problems. Appl. Sci. 2021;11:1286. doi: 10.3390/app11031286. DOI

Trojovská E., Dehghani M., Trojovský P. Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm. IEEE Access. 2022;10:49445–49473. doi: 10.1109/ACCESS.2022.3172789. DOI

Wolpert D.H., Macready W.G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1997;1:67–82.

Kennedy J., Eberhart R. Particle swarm optimization; Proceedings of the ICNN ’95—International Conference on Neural Networks; Perth, WA, Australia. 27 November–1 December 1995; pp. 1942–1948.

Dorigo M., Maniezzo V., Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 1996;26:29–41. doi: 10.1109/3477.484436. PubMed DOI

Karaboga D., Basturk B. Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems; Proceedings of the 12th International Fuzzy Systems Association World Congress (IFSA 2007); Cancun, Mexico. 18–21 June 2007; Berlin/Heidelberg, Germany: Springer; 2007. pp. 789–798.

Yang X.-S. Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2010;2:78–84. doi: 10.1504/IJBIC.2010.032124. DOI

Dehghani M., Montazeri Z., Trojovská E., Trojovský P. Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl.-Based Syst. 2023;259:110011. doi: 10.1016/j.knosys.2022.110011. DOI

Mirjalili S., Mirjalili S.M., Lewis A. Grey Wolf Optimizer. Adv. Eng. Softw. 2014;69:46–61. doi: 10.1016/j.advengsoft.2013.12.007. DOI

Braik M., Hammouri A., Atwan J., Al-Betar M.A., Awadallah M.A. White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowl.-Based Syst. 2022;243:108457. doi: 10.1016/j.knosys.2022.108457. DOI

Jiang Y., Wu Q., Zhu S., Zhang L. Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems. Expert Syst. Appl. 2022;188:116026. doi: 10.1016/j.eswa.2021.116026. DOI

Trojovský P., Dehghani M. A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior. Sci. Rep. 2023;13:8775. doi: 10.1038/s41598-023-35863-5. PubMed DOI PMC

Faramarzi A., Heidarinejad M., Mirjalili S., Gandomi A.H. Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Syst. Appl. 2020;152:113377. doi: 10.1016/j.eswa.2020.113377. DOI

Givi H., Dehghani M., Hubálovský Š. Red Panda Optimization Algorithm: An Effective Bio-Inspired Metaheuristic Algorithm for Solving Engineering Optimization Problems. IEEE Access. 2023;11:57203–57227. doi: 10.1109/ACCESS.2023.3283422. DOI

Abdollahzadeh B., Gharehchopogh F.S., Mirjalili S. African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Comput. Ind. Eng. 2021;158:107408. doi: 10.1016/j.cie.2021.107408. DOI

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. Simul. 2022;192:84–110. doi: 10.1016/j.matcom.2021.08.013. DOI

Chopra N., Ansari M.M. Golden Jackal Optimization: A Novel Nature-Inspired Optimizer for Engineering Applications. Expert Syst. Appl. 2022;198:116924. doi: 10.1016/j.eswa.2022.116924. DOI

Trojovský P., Dehghani M. Subtraction-Average-Based Optimizer: A New Swarm-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics. 2023;8:149. doi: 10.3390/biomimetics8020149. PubMed DOI PMC

Mirjalili S., Lewis A. The whale optimization algorithm. Adv. Eng. Softw. 2016;95:51–67. doi: 10.1016/j.advengsoft.2016.01.008. DOI

Trojovský P., Dehghani M. Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications. Sensors. 2022;22:855. doi: 10.3390/s22030855. PubMed DOI PMC

Kaur S., Awasthi L.K., Sangal A.L., Dhiman G. Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Eng. Appl. Artif. Intell. 2020;90:103541. doi: 10.1016/j.engappai.2020.103541. DOI

Abualigah L., Abd Elaziz M., Sumari P., Geem Z.W., Gandomi A.H. Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Syst. Appl. 2022;191:116158. doi: 10.1016/j.eswa.2021.116158. DOI

Goldberg D.E., Holland J.H. Genetic Algorithms and Machine Learning. Mach. Learn. 1988;3:95–99. doi: 10.1023/A:1022602019183. DOI

Storn R., Price K. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 1997;11:341–359. doi: 10.1023/A:1008202821328. DOI

De Castro L.N., Timmis J.I. Artificial immune systems as a novel soft computing paradigm. Soft Comput. 2003;7:526–544. doi: 10.1007/s00500-002-0237-z. DOI

Reynolds R.G. An introduction to cultural algorithms; Proceedings of the Third Annual Conference on Evolutionary Programming; San Diego, CA, USA. 24–26 February 1994; Singapore: World Scientific; 1994. pp. 131–139.

Koza J.R., Koza J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Volume 1 MIT Press; Cambridge, MA, USA: 1992.

Beyer H.-G., Schwefel H.-P. Evolution strategies–a comprehensive introduction. Nat. Comput. 2002;1:3–52. doi: 10.1023/A:1015059928466. DOI

Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science. 1983;220:671–680. doi: 10.1126/science.220.4598.671. PubMed DOI

Hatamlou A. Black hole: A new heuristic optimization approach for data clustering. Inf. Sci. 2013;222:175–184. doi: 10.1016/j.ins.2012.08.023. DOI

Mirjalili S., Mirjalili S.M., Hatamlou A. Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Comput. Appl. 2016;27:495–513. doi: 10.1007/s00521-015-1870-7. DOI

Dehghani M., Samet H. Momentum search algorithm: A new meta-heuristic optimization algorithm inspired by momentum conservation law. SN Appl. Sci. 2020;2:1720. doi: 10.1007/s42452-020-03511-6. DOI

Dehghani M., Montazeri Z., Dhiman G., Malik O., Morales-Menendez R., Ramirez-Mendoza R.A., Dehghani A., Guerrero J.M., Parra-Arroyo L. A spring search algorithm applied to engineering optimization problems. Appl. Sci. 2020;10:6173. doi: 10.3390/app10186173. DOI

Rashedi E., Nezamabadi-Pour H., Saryazdi S. GSA: A gravitational search algorithm. Inf. Sci. 2009;179:2232–2248. doi: 10.1016/j.ins.2009.03.004. DOI

Pereira J.L.J., Francisco M.B., Diniz C.A., Oliver G.A., Cunha Jr S.S., Gomes G.F. Lichtenberg algorithm: A novel hybrid physics-based meta-heuristic for global optimization. Expert Syst. Appl. 2021;170:114522. doi: 10.1016/j.eswa.2020.114522. DOI

Eskandar H., Sadollah A., Bahreininejad A., Hamdi M. Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput. Struct. 2012;110:151–166. doi: 10.1016/j.compstruc.2012.07.010. DOI

Faramarzi A., Heidarinejad M., Stephens B., Mirjalili S. Equilibrium optimizer: A novel optimization algorithm. Knowl.-Based Syst. 2020;191:105190. doi: 10.1016/j.knosys.2019.105190. DOI

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. 2019;7:66084–66109. doi: 10.1109/ACCESS.2019.2918406. DOI

Cuevas E., Oliva D., Zaldivar D., Pérez-Cisneros M., Sossa H. Circle detection using electro-magnetism optimization. Inf. Sci. 2012;182:40–55. doi: 10.1016/j.ins.2010.12.024. DOI

Kaveh A., Dadras A. A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Adv. Eng. Softw. 2017;110:69–84. doi: 10.1016/j.advengsoft.2017.03.014. DOI

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. 2019;101:646–667. doi: 10.1016/j.future.2019.07.015. DOI

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. 2021;51:1531–1551. doi: 10.1007/s10489-020-01893-z. DOI

Rao R.V., Savsani V.J., Vakharia D. Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput.-Aided Des. 2011;43:303–315. doi: 10.1016/j.cad.2010.12.015. DOI

Matoušová I., Trojovský P., Dehghani M., Trojovská E., Kostra J. Mother optimization algorithm: A new human-based metaheuristic approach for solving engineering optimization. Sci. Rep. 2023;13:10312. doi: 10.1038/s41598-023-37537-8. PubMed DOI PMC

Dehghani M., Mardaneh M., Guerrero J.M., Malik O.P., Ramirez-Mendoza R.A., Matas J., Vasquez J.C., Parra-Arroyo L. A new “Doctor and Patient” optimization algorithm: An application to energy commitment problem. Appl. Sci. 2020;10:5791. doi: 10.3390/app10175791. DOI

Dehghani M., Mardaneh M., Malik O. FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems. J. Oper. Autom. Power Eng. 2020;8:57–64.

Trojovský P., Dehghani M. A new optimization algorithm based on mimicking the voting process for leader selection. PeerJ Comput. Sci. 2022;8:e976. doi: 10.7717/peerj-cs.976. PubMed DOI PMC

Dehghani M., Trojovská E., Zuščák T. A new human-inspired metaheuristic algorithm for solving optimization problems based on mimicking sewing training. Sci. Rep. 2022;12:17387. doi: 10.1038/s41598-022-22458-9. PubMed DOI PMC

Ayyarao T.L., RamaKrishna N., Elavarasam R.M., Polumahanthi N., Rambabu M., Saini G., Khan B., Alatas B. War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization. IEEE Access. 2022;10:25073–25105. doi: 10.1109/ACCESS.2022.3153493. DOI

Braik M., Ryalat M.H., Al-Zoubi H. A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves. Neural Comput. Appl. 2022;34:409–455. doi: 10.1007/s00521-021-06392-x. DOI

Dehghani M., Trojovský P. Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization. Sensors. 2021;21:4567. doi: 10.3390/s21134567. PubMed DOI PMC

Trojovská E., Dehghani M. A new human-based metahurestic optimization method based on mimicking cooking training. Sci. Rep. 2022;12:14861. doi: 10.1038/s41598-022-19313-2. PubMed DOI PMC

Trojovská E., Dehghani M., Leiva V. Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering. Biomimetics. 2023;8:239. doi: 10.3390/biomimetics8020239. PubMed DOI PMC

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. 2020;11:1501–1529. doi: 10.1007/s13042-019-01053-x. DOI

Al-Betar M.A., Alyasseri Z.A.A., Awadallah M.A., Abu Doush I. Coronavirus herd immunity optimizer (CHIO) Neural Comput. Appl. 2021;33:5011–5042. doi: 10.1007/s00521-020-05296-6. PubMed DOI PMC

Dehghani M., Mardaneh M., Guerrero J.M., Malik O., Kumar V. Football game based optimization: An application to solve energy commitment problem. Int. J. Intell. Eng. Syst. 2020;13:514–523. doi: 10.22266/ijies2020.1031.45. DOI

Moghdani R., Salimifard K. Volleyball premier league algorithm. Appl. Soft Comput. 2018;64:161–185. doi: 10.1016/j.asoc.2017.11.043. DOI

Dehghani M., Montazeri Z., Givi H., Guerrero J.M., Dhiman G. Darts game optimizer: A new optimization technique based on darts game. Int. J. Intell. Eng. Syst. 2020;13:286–294. doi: 10.22266/ijies2020.1031.26. DOI

Dehghani M., Montazeri Z., Malik O.P., Ehsanifar A., Dehghani A. OSA: Orientation search algorithm. Int. J. Ind. Electron. Control Optim. 2019;2:99–112.

Zeidabadi F.A., Dehghani M. POA: Puzzle Optimization Algorithm. Int. J. Intell. Eng. Syst. 2022;15:273–281.

Dehghani M., Montazeri Z., Malik O.P. DGO: Dice game optimizer. Gazi Univ. J. Sci. 2019;32:871–882. doi: 10.35378/gujs.484643. DOI

Doumari S.A., Givi H., Dehghani M., Malik O.P. Ring Toss Game-Based Optimization Algorithm for Solving Various Optimization Problems. Int. J. Intell. Eng. Syst. 2021;14:545–554. doi: 10.22266/ijies2021.0630.46. DOI

Montazeri Z., Niknam T., Aghaei J., Malik O.P., Dehghani M., Dhiman G. Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience. Biomimetics. 2023;8:386. doi: 10.3390/biomimetics8050386. PubMed DOI PMC

Dehghani M., Montazeri Z., Saremi S., Dehghani A., Malik O.P., Al-Haddad K., Guerrero J.M. HOGO: Hide objects game optimization. Int. J. Intell. Eng. Syst. 2020;13:216–225. doi: 10.22266/ijies2020.0831.19. DOI

Rauner F., Maclean R. Handbook of Technical and Vocational Education and Training Research. Volume 49 Springer; Berlin/Heidelberg, Germany: 2008.

Billett S. Vocational Education: Purposes, Traditions and Prospects. Springer Science & Business Media; Berlin/Heidelberg, Germany: 2011.

Awad N., Ali M., Liang J., Qu B., Suganthan P., Definitions P. Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization. Nanyang Technological University; Singapore: 2016. Technical Report.

Wilcoxon F. Breakthroughs in Statistics. Springer; Berlin/Heidelberg, Germany: 1992. Individual comparisons by ranking methods; pp. 196–202.

Das S., Suganthan P.N. Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems. Jadavpur University, India and Nanyang Technological University; Singapore: 2010. pp. 341–359. Technical Report.

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...