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

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

doi:10.1590/1806-9649-2022v29e099

Nilo Antonio de Souza Sampaio

Universidade do Estado do Rio de Janeiro – UERJ, Faculdade de Tecnologia, Resende, RJ, Brazil. E-mail: ;

http://orcid.org/0000-0002-6168-785X

José Salvador da Motta Reis

Universidade Estadual Paulista – UNESP, Faculdade de Engenharia de Guaratinguetá, Guaratinguetá, SP, Brazil. E-mail: ;

http://orcid.org/0000-0003-1953-9500

Maximilian Espuny

Universidade Estadual Paulista – UNESP, Faculdade de Engenharia de Guaratinguetá, Guaratinguetá, SP, Brazil. E-mail: ;

http://orcid.org/0000-0002-3189-0355

Ronald Paland Cardoso

Universidade do Estado do Rio de Janeiro – UERJ, Faculdade de Tecnologia, Resende, RJ, Brazil. E-mail: ;

http://orcid.org/0000-0002-7335-2280

Fabricio Maciel Gomes

Universidade de São Paulo – USP, Escola de Engenharia de Lorena, Lorena, SP, Brazil. E-mail: ; ;

Felix Monteiro Pereira

Universidade de São Paulo – USP, Escola de Engenharia de Lorena, Lorena, SP, Brazil. E-mail: ; ;

Luís César Ferreira

Centro Federal de Educação Tecnológica Celso Suckow da Fonseca – CEFET-RJ, Rio de Janeiro, RJ, Brazil. E-mail:

Motta Barbosa

Centro Federal de Educação Tecnológica Celso Suckow da Fonseca – CEFET-RJ, Rio de Janeiro, RJ, Brazil. E-mail:

http://orcid.org/0000-0003-4739-4556

Gilberto Santos

Polytechnic Institute of Cavado and Ave – IPCA, Barcelos, Portugal. E-mail:

http://orcid.org/0000-0001-9268-3272

Messias Borges Silva

Universidade Estadual Paulista – UNESP, Faculdade de Engenharia de Guaratinguetá, Guaratinguetá, SP, Brazil. E-mail: ;

Universidade de São Paulo – USP, Escola de Engenharia de Lorena, Lorena, SP, Brazil. E-mail: ; ;

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Figura 1
Research method.

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Figura 2
Phases in Growth of Publications.

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Figura 3
Development Trends of the research themes.

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Figura 4
Analysis of Main Components.

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Figura 5
Groups of Research Gaps.

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Table 1
Development of research themes.

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Table 2
Research Trends.

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# Title Authors (year) Scientific Gap 1 A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm Askarzadeh (2016a) Analyze the advantages that the meta-heuristic optimizer raven search algorithm provides 2 Symbiotic organism search optimization based task scheduling in cloud computing environment Abdullahi et al. (2016) Analyze the advantages of the search algorithm symbiotic organism scheduling 3 A review of metaheuristic scheduling techniques in cloud computing Kalra & Singh (2015) Identify the applicability of meta-heuristic scheduling algorithm for cloud environments 4 An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems Osaba et. Al (2016) Propose a descriptive reversal of the improved discrete bat algorithm for process optimization 5 A social spider algorithm for global optimization Yu & Li (2015) Identify the feasibility of the swarm intelligence algorithm inspired by the social nature of spiders in optimization processes 6 Economic dispatch using chaotic bat algorithm Adarsh et al. (2016) Propose a comparison of chaotic sequences for optimization problems 7 Harris hawks optimization: algorithm and applications Heidari et al. (2017) Propose the use of the harris hawks optimizer method for problem solving 8 Simultaneous reconfiguration, optimal placement of dstatcom, and photovoltaic array in a distribution system based on fuzzy-aco approach Bagheri Tolabi et al. (2015) Evaluate the feasibility of combining a multi-objective fuzzy effect with ant colony optimization 9 A modified flower pollination algorithm for global optimization Nabil (2016) Propose an improved variant of the flower pollination algorithm in solving optimization problems 10 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 Tien Bui et al. (2016) Identify the advantages of artificial intelligence based on fuzzy neural inference system in process optimization 11 Optimal integration and planning of renewable distributed generation in the power distribution networks: a review of analytical techniques Ehsan & Yang (2018) Propose the use of artificial intelligence to optimize the planning and distribution of renewable energy 12 Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization Silva et al. (2015) Evaluate the feasibility of hybrid methods in the optimization of processes with multiple output variables 13 A new feature selection method to improve the document clustering using particle swarm optimization algorithm Abualigah et al. (2018) Evaluate the benefits of bayesian calibration to build energy simulation 14 Chaotic fruit fly optimization algorithm Mitić et al. (2015) Implement new fruit fly algorithm and optimization method in practical studies 15 Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications Dhiman & Kumar (2017) Systematize the new metaheuristic algorithm called spotted hyena optimizer and identify new applications 16 Emperor penguin optimizer: a bio-inspired algorithm for engineering problems Dhiman & Kumar (2018) Identify the difficulties when implementing the emperor penguin optimizer optimization algorithm in engineering problems 17 Computational intelligence techniques for hvac systems: a review Ahmad et al. (2016) Systematize the benefits of using computational intelligence in the optimization of problems in heating, ventilation and air-conditioning systems 18 An algorithm for many-objective optimization with reduced objective computations: a study in differential evolution Bandyopadhyay & Mukherjee (2015) Implement the algorithm for many-objective optimization in practical studies 19 A survey of multiobjective evolutionary clustering Mukhopadhyay et al. (2015) Describe evolutionary clustering techniques in the optimization of processes with multiple output variables 20 An improved opposition-based sine cosine algorithm for global optimization Abd Elaziz et al. (2017) Identify the possibilities of applicability of the cosine sine opposition-based algorithm 21 An efficient chaotic water cycle algorithm for optimization tasks Heidari et al. (2017) Identify the benefits in optimizing processes with multiple output variables by implementing the water cycle algorithm 22 Metaheuristics in structural optimization and discussions on harmony search algorithm Saka et al. (2016) Describe the benefits of applying meta-heuristic algorithms to the optimization of everyday problems 23 Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation Caraveo et al. (2016) Analyze and describe the multi-agent systems techniques for optimizing climate control systems 24 Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures Sadollah et al. (2015) Propose the implementation of the fuzzy bco method 25 Parameters identification of photovoltaic models using hybrid adaptive nelder-mead simplex algorithm based on eagle strategy Chen et al. (2016) Propose the operation of an adaptive nelder-mead simplex enhanced with the artificial bee colony metaheuristic on different types of systems 26 Use of meta-heuristic techniques in rainfall-runoffmodelling Chau (2017) Analyze and describe the benefits of using rainfall runoff modeling metaheuristic techniques in process optimization 27 A power efficient cluster-based routing algorithm for wireless sensor networks: honeybees swarm intelligence based approach Ari et al. (2016) Identify the benefits of the cluster-based routing protocol called abc-sd 28 Smart artificial firefly colony algorithm-based support vector regression for enhanced forecasting in civil engineering Chou & Pham (2015) Propose a new support vector regression based on the firefly algorithm in process optimization 29 A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects Karagöz & Yıldız (2017) Map the search fields of process optimization algorithms 30 A genetic algorithm for energy-efficiency in job-shop scheduling Salido et al. (2016) The application refers exclusively to the use of clusters for specific application in energy consumption in sensors 31 Algorithmic design issues in adaptive differential evolution schemes: review and taxonomy Al-Dabbagh et al. (2018) Identify the benefits of algorithmic design schemes that have been used in variants of differential evolution 32 Capacitor placement in distribution systems for power loss reduction and voltage improvement: a new methodology Askarzadeh (2016b) Identify applications for the newly developed metaheuristic, raven search algorithm 33 High-dimensional feature selection via feature grouping: a variable neighborhood search approach García-Torres et al. (2016) Identify the applicability of the neighborhood variable search algorithm in multiple output variable processes 34 Particle swarm optimization algorithm for capacitor allocation problem in distribution systems with wind turbine generators Ramadan et al. (2017) Map the new applications for the particle swarm optimization method 35 Design of a cost-effective wind/photovoltaic/hydrogen energy system for supplying a desalination unit by a heuristic approach Maleki et al. (2016) Implement a model of a hybrid photovoltaic, wind and hydrogen desalination system to increase the availability of fresh water 36 A modified symbiotic organisms search algorithm for large scale economic dispatch problem with valve-point effects Secui (2016) Identify the weaknesses of modified symbiotic organisms search algorithm 37 Adaptive dimensional search: a new metaheuristic algorithm for discrete truss sizing optimization Hasançebi & Azad (2015) Identify the weaknesses of the adaptive dimensional search algorithm 38 Toward improved mechanical, tribological, corrosion and in-vitro bioactivity properties of mixed oxide nanotubes on ti–6al–7nb implant using multi-objective pso Rafieerad et al. (2017) Identify the applicability of the particle swarm optimization algorithm in processes with multiple output variables 39 Glossary of metaheuristic algorithms Rajpurohit et al. (2017) Systematize the algorithms that can be applied to processes with multiple output variables 40 A destroy and repair algorithm for the bike sharing rebalancing problem Dell׳Amico et al. (2016) Identify the applicability of the destroy and repair algorithm in process optimization 41 Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions Fallah et al. (2018) Systematize main intelligent computational methods in power load forecasting in smart grids 42 Ant lion optimization algorithm to control side lobe level and null depths in linear antenna arrays Saxena & Kothari (2016) Identify the benefits of the ant lion optimization algorithm in electromagnetics 43 A new rooted tree optimization algorithm for economic dispatch with valve-point effect Labbi et al. (2016) Identify the applicability of the root tree optimization algorithm in process optimization 44 Design optimization of real world steel space frames using artificial bee colony algorithm with levy flight distribution Aydoğdu et al. (2016) Identify the applicability of the artificial bee colony algorithm in multiple output variable processes 45 A research survey: review of ai solution strategies of job shop scheduling problem Çaliş & Bulkan (2015) Map the main causes of job shop scheduling problems 46 A novel approach for multispectral satellite image classification based on the bat algorithm Senthilnath et al. (2016) Identify the applicability of the bat algorithm in processes with multiple output variables 47 Cuckoo optimization algorithm with penalty function for combined heat and power economic dispatch problem Mellal & Williams (2015) Identify the applicability of the cuckoo optimization algorithm in processes with multiple output variables 48 Binary grasshopper optimisation algorithm approaches for feature selection problems Mafarja et al. (2019) Identify the applicability of the grasshopper optimization algorithm in processes with multiple output variables 49 Whale optimization algorithm based optimal reactive power dispatch: a case study of the algerian power system Medani et al. (2018) Identify the applicability of the whale optimization algorithm in processes with multiple output variables 50 A multi-verse optimizer approach for feature selection and optimizing svm parameters based on a robust system architecture Faris et al. (2018) Identify which are the most relevant trends in process optimization

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PERIOD		1	2012 and 2013	2	2014 and 2015	3	2016 and 2017	4	2018 and 2019	(2-1)	(3-2)	(4-3)	(4-2)
CLUSTER		1	2012 and 2013	2	2014 and 2015	3	2016 and 2017	4	2018 and 2019	(2-1)	(3-2)	(4-3)	(4-2)
1	ALGORITHM VARIATIONS	87	64.44%	132	75.00%	215	76.51%	159	68.53%	10.56%	1.51%	-7.98%	-6.47%
2	ARTIFICIAL INTELLIGENCE	18	13.33%	30	17.05%	53	18.86%	35	15.09%	3.71%	1.82%	-3.78%	-1.96%
3	COMPUTER RESOURCES	31	22.96%	40	22.73%	73	25.98%	73	31.47%	-0.24%	3.25%	5.49%	8.74%
4	CONTROL METHODS	10	7.41%	14	7.95%	26	9.25%	25	10.78%	0.55%	1.30%	1.52%	2.82%
5	METAHEURISTIC TECHNIQUES	79	58.52%	88	50.00%	139	49.47%	108	46.55%	-8.52%	-0.53%	-2.91%	-3.45%
6	NETWORKS	9	6.67%	12	6.82%	42	14.95%	32	13.79%	0.15%	8.13%	-1.15%	6.97%
7	PROCESS OPTIMISATION	85	62.96%	128	72.73%	225	80.07%	192	82.76%	9.76%	7.34%	2.69%	10.03%
8	SCHEDULING	16	11.85%	31	17.61%	41	14.59%	45	19.40%	5.76%	-3.02%	4.81%	1.78%
9	SEARCH METHODS	30	22.22%	32	18.18%	46	16.37%	45	19.40%	-4.04%	-1.81%	3.03%	1.21%
10	SYSTEM TYPES	14	10.37%	34	19.32%	75	26.69%	69	29.74%	8.95%	7.37%	3.05%	10.42%
11	MISCELLANEOUS PROBLEMS	25	18.52%	32	18.18%	58	20.64%	45	19.40%	-0.34%	2.46%	-1.24%	1.21%
	TOTAL DOCUMENTS		135		176		281		232

Project	(Abd Elaziz et al., 2017Abd 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. http://dx.doi.org/10.1016/j.eswa.2017.07... ; Adarsh et al., 2016Adarsh, 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. http://dx.doi.org/10.1016/j.energy.2015.... ; Askarzadeh, 2016bAskarzadeh, 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. http://dx.doi.org/10.1049/iet-gtd.2016.0... ; Aydoğdu et al., 2016Aydoğdu, 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. http://dx.doi.org/10.1016/j.advengsoft.2... ; Çaliş & Bulkan, 2015Çaliş, 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. http://dx.doi.org/10.1007/s10845-013-083... ; Chen et al., 2016Chen, 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. http://dx.doi.org/10.1016/j.apenergy.201... ; Chou & Pham, 2015Chou, 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. http://dx.doi.org/10.1111/mice.12121... ; Ehsan & Yang, 2018Ehsan, 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. http://dx.doi.org/10.1016/j.apenergy.201... ; Fallah et al., 2018Fallah, 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. http://dx.doi.org/10.3390/en11030596... ; García-Torres et al., 2016García-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. http://dx.doi.org/10.1016/j.ins.2015.07.... ; Heidari et al., 2019Heidari, 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. http://dx.doi.org/10.1016/j.future.2019.... ; Karagöz & Yıldız, 2017Karagöz, 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. http://dx.doi.org/10.1504/IJVD.2017.0825... ; Labbi et al., 2016Labbi, 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. http://dx.doi.org/10.1016/j.ijepes.2016.... ; Maleki et al., 2016Maleki, 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. http://dx.doi.org/10.1016/j.solener.2016... ; Mukhopadhyay et al., 2015Mukhopadhyay, 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. http://dx.doi.org/10.1145/2742642... ; Nabil, 2016Nabil, 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. http://dx.doi.org/10.1016/j.eswa.2016.03... ; Osaba et al., 2016Osaba, 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. http://dx.doi.org/10.1016/j.engappai.201... ; Rajpurohit et al., 2017Rajpurohit, 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 et al., 2017Ramadan, 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. http://dx.doi.org/10.1016/j.ijepes.2016.... ; Sadollah et al., 2015Sadollah, 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. http://dx.doi.org/10.1016/j.compstruc.20... ; Salido et al., 2016Salido, 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. http://dx.doi.org/10.1007/s00170-015-798... )
Test	(Dell׳Amico et al., 2016Dell׳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. http://dx.doi.org/10.1016/j.cor.2016.01.... ; Faris et al., 2018Faris, 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. http://dx.doi.org/10.1007/s00521-016-281... ; Kalra & Singh, 2015Kalra, 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. http://dx.doi.org/10.1016/j.eij.2015.07.... ; Mafarja et al., 2019Mafarja, 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. http://dx.doi.org/10.1016/j.eswa.2018.09... ; Medani et al., 2018Medani, 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. http://dx.doi.org/10.1016/j.epsr.2017.09... ; Mellal & Williams, 2015Mellal, 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. http://dx.doi.org/10.1016/j.energy.2015.... ; Rafieerad et al., 2017Rafieerad, 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. http://dx.doi.org/10.1016/j.jmbbm.2016.1... ; Senthilnath et al., 2016Senthilnath, 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. http://dx.doi.org/10.1109/LGRS.2016.2530... ; Yu & Li, 2015Yu, 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. http://dx.doi.org/10.1016/j.asoc.2015.02... )
Implementation	(Ahmad et al., 2016Ahmad, 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. http://dx.doi.org/10.1007/s12273-016-028... ; Bandyopadhyay & Mukherjee, 2015Bandyopadhyay, 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. http://dx.doi.org/10.1109/TEVC.2014.2332... ; Dhiman & Kumar, 2017Dhiman, 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. http://dx.doi.org/10.1016/j.advengsoft.2... ; Mitić et al., 2015Mitić, 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. http://dx.doi.org/10.1016/j.knosys.2015.... )
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Brasil

Brasil

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

Contribuições para o futuro da metaheurística nos contornos do desenvolvimento científico

Abstract