Gestão & Produção
https://www.gestaoeproducao.com/article/doi/10.1590/1806-9649-2022v29e099
Gestão & Produção
Artigo Original

Contributions to the future of metaheuristics in the contours of scientific development

Nilo Antonio de Souza Sampaio; José Salvador da Motta Reis; Maximilian Espuny; Ronald Paland Cardoso; Fabricio Maciel Gomes; Felix Monteiro Pereira; Luís César Ferreira; Motta Barbosa; Gilberto Santos; Messias Borges Silva

Downloads: 0
Views: 647

Abstract

Abstract: Metaheuristic algorithms solve optimisation problems by identifying the best combination among a set of variables to enhance a function. Within metaheuristics, the main purpose of this work is that of showing the development of research issues about processes related to optimisation and metaheuristics, with a focus on the projection of those issues with greater possibility of development. Optimization processes is one of the most studied fields in artificial intelligence, optimization, logistics, and other applications The main contributions of this work were the identification of the main issues contained in the themes of process optimization and metaheuristics; an analysis of the expansion and retraction of the aforementioned theme; an understanding of convergence and divergence; and an analysis of the stages of development as presented in the gaps of the fifty most commonly mentioned articles. The main finding was to analyze the development of research topics on optimization processes and metaheuristics, focusing on projecting the topics most likely to develop.

Keywords

Optimization, Modelling, Algorithm, Metaheuristics

Referências

Abd Elaziz, M., Oliva, D., & Xiong, S. (2017). An improved Opposition-Based Sine Cosine Algorithm for global optimization. Expert Systems with Applications, 90, 484-500. http://dx.doi.org/10.1016/j.eswa.2017.07.043.

Abdullahi, M., Ngadi, M. A., & Abdulhamid, S. M. (2016). Symbiotic Organism Search optimization based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640-650. http://dx.doi.org/10.1016/j.future.2015.08.006.

Abualigah, L. M., Khader, A. T., & Hanandeh, E. S. (2018). A new feature selection method to improve the document clustering using particle swarm optimization algorithm. Journal of Computational Science, 25, 456-466. http://dx.doi.org/10.1016/j.jocs.2017.07.018.

Adarsh, B. R., Raghunathan, T., Jayabarathi, T., & Yang, X.-S. (2016). Economic dispatch using chaotic bat algorithm. Energy, 96, 666-675. http://dx.doi.org/10.1016/j.energy.2015.12.096.

Ahmad, M. W., Mourshed, M., Yuce, B., & Rezgui, Y. (2016). Computational intelligence techniques for HVAC systems: a review. Building Simulation, 9(4), 359-398. http://dx.doi.org/10.1007/s12273-016-0285-4.

Al-Dabbagh, R. D., Neri, F., Idris, N., & Baba, M. S. (2018). Algorithmic design issues in adaptive differential evolution schemes: review and taxonomy. Swarm and Evolutionary Computation, 43, 284-311. http://dx.doi.org/10.1016/j.swevo.2018.03.008.

Alvarenga, A. B. C. S., Espuny, M., Reis, J. S. da M., Silva, F. D. O., Sampaio, N. A. de S., Nunhes, T. V., Barbosa, L. C. F. M., Santos, G., & Oliveira, O. J. (2021). The main perspectives of the quality of life of students in the secondary cycle: an overview of the opportunities, challenges and elements of greatest impact. International Journal of Qualitative Research, 15(3). http://dx.doi.org/10.24874/IJQR15.03-19.

Álvarez, A., & Munari, P. (2016). Metaheuristic approaches for the vehicle routing problem with time windows and multiple deliverymen. Gestão & Produção, 23(2), 279-293. http://dx.doi.org/10.1590/0104-530x2359-15.

Andrade, P. R. de L., Steiner, M. T. A., & Góes, A. R. T. (2019). Optimization in timetabling in schools using a mathematical model, local search and Iterated Local Search procedures. Gestão & Produção, 26(4), e3421. http://dx.doi.org/10.1590/0104-530x3241-19.

Ari, A. A. A., Yenke, B. O., Labraoui, N., Damakoa, I., & Gueroui, A. (2016). A power efficient cluster-based routing algorithm for wireless sensor networks: honeybees swarm intelligence based approach. Journal of Network and Computer Applications, 69, 77-97. http://dx.doi.org/10.1016/j.jnca.2016.04.020.

Askarzadeh, A. (2016a). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures, 169, 1-12. http://dx.doi.org/10.1016/j.compstruc.2016.03.001.

Askarzadeh, A. (2016b). Capacitor placement in distribution systems for power loss reduction and voltage improvement: a new methodology. IET Generation, Transmission & Distribution, 10(14), 3631-3638. http://dx.doi.org/10.1049/iet-gtd.2016.0419.

Aydoğdu, İ., Akın, A., & Saka, M. P. (2016). Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Advances in Engineering Software, 92, 1-14. http://dx.doi.org/10.1016/j.advengsoft.2015.10.013.

Bagheri Tolabi, H., Ali, M. H., & Rizwan, M. (2015). Simultaneous reconfiguration, optimal placement of DSTATCOM, and photovoltaic array in a distribution system based on fuzzy-aco approach. IEEE Transactions on Sustainable Energy, 6(1), 210-218. http://dx.doi.org/10.1109/TSTE.2014.2364230.

Bandyopadhyay, S., & Mukherjee, A. (2015). An algorithm for many-objective optimization with reduced objective computations: a study in differential evolution. IEEE Transactions on Evolutionary Computation, 19(3), 400-413. http://dx.doi.org/10.1109/TEVC.2014.2332878.

Çaliş, B., & Bulkan, S. (2015). A research survey: review of AI solution strategies of job shop scheduling problem. Journal of Intelligent Manufacturing, 26(5), 961-973. http://dx.doi.org/10.1007/s10845-013-0837-8.

Caraveo, C., Valdez, F., & Castillo, O. (2016). Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Applied Soft Computing, 43, 131-142. http://dx.doi.org/10.1016/j.asoc.2016.02.033.

Chau, K. (2017). Use of meta-heuristic techniques in rainfall-runoff modelling. Water, 9(3), 186. http://dx.doi.org/10.3390/w9030186.

Chen, V. C. P., Tsui, K.-L., Barton, R. R., & Meckesheimer, M. (2006). A review on design, modeling and applications of computer experiments. IIE Transactions, 38(4), 273-291. http://dx.doi.org/10.1080/07408170500232495.

Chen, Z., Wu, L., Lin, P., Wu, Y., & Cheng, S. (2016). Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy. Applied Energy, 182, 47-57. http://dx.doi.org/10.1016/j.apenergy.2016.08.083.

Cheng, T., Pan, Y., Hao, M., Wang, Y., & Bryant, S. H. (2014). PubChem applications in drug discovery: a bibliometric analysis. Drug Discovery Today, 19(11), 1751-1756. http://dx.doi.org/10.1016/j.drudis.2014.08.008. PMid:25168772.

Chou, J.-S., & Pham, A.-D. (2015). Smart artificial firefly colony algorithm-based support vector regression for enhanced forecasting in civil engineering. Computer-Aided Civil and Infrastructure Engineering, 30(9), 715-732. http://dx.doi.org/10.1111/mice.12121.

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: a new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609-1630. http://dx.doi.org/10.1002/asi.22688.

Companez, N., & Aleti, A. (2016). Can Monte-Carlo Tree Search learn to sacrifice? Journal of Heuristics, 22(6), 783-813. http://dx.doi.org/10.1007/s10732-016-9320-y.

Cristino, T. M., Faria, A., No. & Costa, A. F. B. (2018). Energy efficiency in buildings: analysis of scientific literature and identification of data analysis techniques from a bibliometric study. Scientometrics, 114(3), 1275-1326. http://dx.doi.org/10.1007/s11192-017-2615-4.

Dabi, Y., Darrigues, L., Katsahian, S., Azoulay, D., De Antonio, M., & Lazzati, A. (2016). Publication trends in bariatric surgery: a bibliometric study. Obesity Surgery, 26(11), 2691-2699. http://dx.doi.org/10.1007/s11695-016-2160-x. PMid:27052317.

Dang, Q.-V., Nguyen, C. T., & Rudová, H. (2019). Scheduling of mobile robots for transportation and manufacturing tasks. Journal of Heuristics, 25(2), 175-213. http://dx.doi.org/10.1007/s10732-018-9391-z.

Dell׳Amico, M., Iori, M., Novellani, S., & Stützle, T. (2016). A destroy and repair algorithm for the Bike sharing Rebalancing Problem. Computers & Operations Research, 71, 149-162. http://dx.doi.org/10.1016/j.cor.2016.01.011.

Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70. http://dx.doi.org/10.1016/j.advengsoft.2017.05.014.

Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20-50. http://dx.doi.org/10.1016/j.knosys.2018.06.001.

Ehsan, A., & Yang, Q. (2018). Optimal integration and planning of renewable distributed generation in the power distribution networks: a review of analytical techniques. Applied Energy, 210, 44-59. http://dx.doi.org/10.1016/j.apenergy.2017.10.106.

Espuny, M., Faria, A., No., Motta Reis, J. S., Santos, S. T., No., Nunhes, T. V., & de Oliveira, O. J. (2021). Building New Paths for Responsible Solid Waste Management. Environmental Monitoring and Assessment, 193(7), 442. http://dx.doi.org/10.1007/s10661-021-09173-0. PMid:34165638.

Fallah, S., Deo, R., Shojafar, M., Conti, M., & Shamshirband, S. (2018). Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions. Energies, 11(3), 596. http://dx.doi.org/10.3390/en11030596.

Faris, H., Hassonah, M. A., Al-Zoubi, A. M., Mirjalili, S., & Aljarah, I. (2018). A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Computing & Applications, 30(8), 2355-2369. http://dx.doi.org/10.1007/s00521-016-2818-2.

García-Torres, M., Gómez-Vela, F., Melián-Batista, B., & Moreno-Vega, J. M. (2016). High-dimensional feature selection via feature grouping: a variable neighborhood search approach. Information Sciences, 326, 102-118. http://dx.doi.org/10.1016/j.ins.2015.07.041.

Garza-Reyes, J. A., Torres Romero, J., Govindan, K., Cherrafi, A., & Ramanathan, U. (2018). A PDCA-based approach to Environmental Value Stream Mapping (E-VSM). Journal of Cleaner Production, 180, 335-348. http://dx.doi.org/10.1016/j.jclepro.2018.01.121.

Gomes, F. M., Pereira, F. M., Silva, A. F., & Silva, M. B. (2019). Multiple response optimization: analysis of genetic programming for symbolic regression and assessment of desirability functions. Knowledge-Based Systems, 179, 21-33. http://dx.doi.org/10.1016/j.knosys.2019.05.002.

Hasançebi, O., & Azad, S. K. (2015). Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization. Computers & Structures, 154, 1-16. http://dx.doi.org/10.1016/j.compstruc.2015.03.014.

Heidari, A. A., Ali Abbaspour, R., & Rezaee Jordehi, A. (2017). An efficient chaotic water cycle algorithm for optimization tasks. Neural Computing & Applications, 28(1), 57-85. http://dx.doi.org/10.1007/s00521-015-2037-2.

Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: algorithm and applications. Future Generation Computer Systems, 97, 849-872. http://dx.doi.org/10.1016/j.future.2019.02.028.

Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. http://dx.doi.org/10.1177/1049732305276687. PMid:16204405.

Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273. http://dx.doi.org/10.1016/j.eij.2015.06.005.

Kalra, M., & Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal, 16(3), 275-295. http://dx.doi.org/10.1016/j.eij.2015.07.001.

Karagöz, S., & Yıldız, A. R. (2017). A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects. International Journal of Vehicle Design, 73(1-3), 179. http://dx.doi.org/10.1504/IJVD.2017.082593.

Kothari, C. R., & Garg, G. (2019). Research methodology methods and techniques. New Delhi: New Age International.

Labbi, Y., Attous, D. B., Gabbar, H. A., Mahdad, B., & Zidan, A. (2016). A new rooted tree optimization algorithm for economic dispatch with valve-point effect. International Journal of Electrical Power & Energy Systems, 79, 298-311. http://dx.doi.org/10.1016/j.ijepes.2016.01.028.

Leoni, R. C., Sampaio, N. A. S., & Corrêa, S. M. (2017). Multivariate analysis applied to air quality study. Revista Brasileira de Meteorologia, 32(2), 235-241. http://dx.doi.org/10.1590/0102-77863220005.

Li, H., Jiang, H.-D., Yang, B., & Liao, H. (2019). An analysis of research hotspots and modeling techniques on carbon capture and storage. The Science of the Total Environment, 687(5), 687-701. http://dx.doi.org/10.1016/j.scitotenv.2019.06.013. PMid:31220722.

Mafarja, M., Aljarah, I., Faris, H., Hammouri, A. I., Al-Zoubi, A. M., & Mirjalili, S. (2019). Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications, 117, 267-286. http://dx.doi.org/10.1016/j.eswa.2018.09.015.

Maleki, A., Pourfayaz, F., & Ahmadi, M. H. (2016). Design of a cost-effective wind/photovoltaic/hydrogen energy system for supplying a desalination unit by a heuristic approach. Solar Energy, 139, 666-675. http://dx.doi.org/10.1016/j.solener.2016.09.028.

Medani, K., Sayah, S., & Bekrar, A. (2018). Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electric Power Systems Research, 163, 696-705. http://dx.doi.org/10.1016/j.epsr.2017.09.001.

Mellal, M. A., & Williams, E. J. (2015). Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem. Energy, 93, 1711-1718. http://dx.doi.org/10.1016/j.energy.2015.10.006.

Mitić, M., Vuković, N., Petrović, M., & Miljković, Z. (2015). Chaotic fruit fly optimization algorithm. Knowledge-Based Systems, 89(August), 446-458. http://dx.doi.org/10.1016/j.knosys.2015.08.010.

Mohamed, A. W. (2017). Solving stochastic programming problems using new approach to Differential Evolution algorithm. Egyptian Informatics Journal, 18(2), 75-86. http://dx.doi.org/10.1016/j.eij.2016.09.002.

Mohamed, A. W., Sabry, H. Z., & Abd-Elaziz, T. (2013). Real parameter optimization by an effective differential evolution algorithm. Egyptian Informatics Journal, 14(1), 37-53. http://dx.doi.org/10.1016/j.eij.2013.01.001.

Mukhopadhyay, A., Maulik, U., & Bandyopadhyay, S. (2015). A Survey of Multiobjective Evolutionary Clustering. ACM Computing Surveys, 47(4), 1-46. http://dx.doi.org/10.1145/2742642.

Nabil, E. (2016). A Modified Flower Pollination Algorithm for Global Optimization. Expert Systems with Applications, 57, 192-203. http://dx.doi.org/10.1016/j.eswa.2016.03.047.

Neumuth, T., Loebe, F., & Jannin, P. (2012). Similarity metrics for surgical process models. Artificial Intelligence in Medicine, 54(1), 15-27. http://dx.doi.org/10.1016/j.artmed.2011.10.001. PMid:22056273.

Osaba, E., Yang, X. S., Diaz, F., Lopez-Garcia, P., & Carballedo, R. (2016). An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems. Engineering Applications of Artificial Intelligence, 48, 59-71. http://dx.doi.org/10.1016/j.engappai.2015.10.006.

Phillips, J. F., Sheff, M., & Boyer, C. B. (2015). The astronomy of Africa’s health systems literature during the MDG era: where are the systems clusters? Global Health, Science and Practice, 3(3), 482-502. http://dx.doi.org/10.9745/GHSP-D-15-00034. PMid:26374806.

Prashar, A. (2017). Adopting PDCA (Plan-Do-Check-Act) cycle for energy optimization in energy-intensive SMEs. Journal of Cleaner Production, 145, 277-293. http://dx.doi.org/10.1016/j.jclepro.2017.01.068.

Rafieerad, A. R., Bushroa, A. R., Nasiri-Tabrizi, B., Kaboli, S. H. A., Khanahmadi, S., Amiri, A., Vadivelu, J., Yusof, F., Basirun, W. J., & Wasa, K. (2017). Toward improved mechanical, tribological, corrosion and in-vitro bioactivity properties of mixed oxide nanotubes on Ti–6Al–7Nb implant using multi-objective PSO. Journal of the Mechanical Behavior of Biomedical Materials, 69, 1-18. http://dx.doi.org/10.1016/j.jmbbm.2016.11.019. PMid:28027481.

Rajpurohit, J., Sharma, T. K., Abraham, A., & Vaishali. (2017). Glossary of metaheuristic algorithms. International Journal of Computer Information Systems and Industrial Management Applications, 9, 181-205.

Ramadan, H. S., Bendary, A. F., & Nagy, S. (2017). Particle swarm optimization algorithm for capacitor allocation problem in distribution systems with wind turbine generators. International Journal of Electrical Power & Energy Systems, 84, 143-152. http://dx.doi.org/10.1016/j.ijepes.2016.04.041.

Reis, J. S. M., Costa, A. C. F., Espuny, M., Batista, W. J., Francisco, F. E., Gonçalves, G. S., Tasinaffo, P. M., Vieira Dias, L. A., Cunha, A. M., & Oliveira, O. J. (2020a). Education 4.0: gaps research between school formation and technological development. In S. Latifi (Ed.), 17th International Conference on Information Technology-New Generations (ITNG 2020) (1st ed., pp. 415-420). Cham: Springer. https://doi.org/10.1007/978-3-030-43020-7_55.

Reis, J. S. M., Silva, F. D. O., Espuny, M., Alexandre, L. G. L., Barbosa, L. C. F. M., Santos, G., Bonassa, A. C. M., Faria, A. M., Sampaio, N. A. de S., & Oliveira, O. J. (2020b). The rapid escalation of publications on Covid-19: a snapshot of trends in the early months to overcome the pandemic and to improve life quality. International Journal of Qualitative Research, 14(3), 951-968. http://dx.doi.org/10.24874/IJQR14.03-19.

Reis, J. S. M., Espuny, M., Nunhes, T. V., Sampaio, N. A. de S., Isaksson, R., de Campos, F. C., & de Oliveira, O. J. (2021). Striding towards sustainability: a framework to overcome challenges and explore opportunities through Industry 4.0. Sustainability, 13(9), 5232. http://dx.doi.org/10.3390/su13095232.

Romasanta, A. K. S., van der Sijde, P., Hellsten, I., Hubbard, R. E., Keseru, G. M., van Muijlwijk-Koezen, J., & de Esch, I. J. P. (2018). When fragments link: a bibliometric perspective on the development of fragment-based drug discovery. Drug Discovery Today, 23(9), 1596-1609. http://dx.doi.org/10.1016/j.drudis.2018.05.004. PMid:29738823.

Sadollah, A., Eskandar, H., Bahreininejad, A., & Kim, J. H. (2015). Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures. Computers & Structures, 149, 1-16. http://dx.doi.org/10.1016/j.compstruc.2014.12.003.

Saka, M. P., Hasançebi, O., & Geem, Z. W. (2016). Metaheuristics in structural optimization and discussions on harmony search algorithm. Swarm and Evolutionary Computation, 28, 88-97. http://dx.doi.org/10.1016/j.swevo.2016.01.005.

Salido, M. A., Escamilla, J., Giret, A., & Barber, F. (2016). A genetic algorithm for energy-efficiency in job-shop scheduling. International Journal of Advanced Manufacturing Technology, 85(5-8), 1303-1314. http://dx.doi.org/10.1007/s00170-015-7987-0.

Santini, A., Ropke, S., & Hvattum, L. M. (2018). A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic. Journal of Heuristics, 24(5), 783-815. http://dx.doi.org/10.1007/s10732-018-9377-x.

Saxena, P., & Kothari, A. (2016). Ant Lion Optimization algorithm to control side lobe level and null depths in linear antenna arrays. AEÜ. International Journal of Electronics and Communications, 70(9), 1339-1349. http://dx.doi.org/10.1016/j.aeue.2016.07.008.

Secui, D. C. (2016). A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects. Energy, 113, 366-384. http://dx.doi.org/10.1016/j.energy.2016.07.056.

Senthilnath, J., Kulkarni, S., Benediktsson, J. A., & Yang, X. S. (2016). A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geoscience and Remote Sensing Letters, 13(4), 599-603. http://dx.doi.org/10.1109/LGRS.2016.2530724.

Silva, A. S., Medeiros, C. F., & Vieira, R. K. (2017). Cleaner Production and PDCA cycle: practical application for reducing the Cans Loss Index in a beverage company. Journal of Cleaner Production, 150, 324-338. http://dx.doi.org/10.1016/j.jclepro.2017.03.033.

Silva, T. M., Fo., Pimentel, B. A., Souza, R. M. C. R., & Oliveira, A. L. I. (2015). Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Systems with Applications, 42(17-18), 6315-6328. http://dx.doi.org/10.1016/j.eswa.2015.04.032.

Tien Bui, D., Pradhan, B., Nampak, H., Bui, Q.-T., Tran, Q.-A., & Nguyen, Q.-P. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317-330. http://dx.doi.org/10.1016/j.jhydrol.2016.06.027.

Varella, C. A. A. (2008). Análise de componentes principais (1ª ed.). Seropédica: UFFRJ.

Xie, L., Merschformann, M., Kliewer, N., & Suhl, L. (2017). Metaheuristics approach for solving personalized crew rostering problem in public bus transit. Journal of Heuristics, 23(5), 321-347. http://dx.doi.org/10.1007/s10732-017-9348-7.

Yu, J. J. Q., & Li, V. O. K. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627. http://dx.doi.org/10.1016/j.asoc.2015.02.014.
 

6241a80ca9539563242ae243 gp Articles

Gest. Prod.

Share this page
Page Sections