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The application of nature-inspired optimization algorithms on the modern management: A systematic literature review and bibliometric analysis

Published online by Cambridge University Press:  21 October 2022

Yi Zhou ,
Weili Xia  and
Jiapeng Dai Open the ORCID record for Jiapeng Dai [Opens in a new window]
Yi Zhou
Affiliation:
School of Management, Northwestern Polytechnical University, Xi'an 710072, China
Weili Xia
Affiliation:
School of Management, Northwestern Polytechnical University, Xi'an 710072, China
Jiapeng Dai*
Affiliation:
School of Government, Nanjing University, Nanjing, 210023, China
*
Author for correspondence: Jiapeng Dai, E-mail: [email protected]
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Abstract

With the expanding adoption of technology and intelligent applications in every aspect of our life, energy, resource, data, and product management are all improving. So, modern management has recently surged to cope with modern societies. Numerous optimization approaches and algorithms are used to effectively optimize the literature while taking into account its many restrictions. With their dependability and superior solution quality for overcoming the numerous barriers to generation, distribution, integration, and management, nature-inspired meta-heuristic optimization algorithms have stood out among these methods. Hence, this article aims to review the application of nature-inspired optimization algorithms to modern management. Besides, the created clusters introduce the top authors in this field. The results showed that nature-inspired optimization algorithms contribute significantly to cost, resource, and energy efficiency. The genetic algorithm is also the most important and widely used method in the previous literature.

Keywords

Data managementenergy managementmodern managementnature-inspired optimization algorithmsorganization managementproduct managementresource management
Type
Research Article
Information
Journal of Management & Organization , Volume 29 , Issue 4: The Role of New ICT-based Systems in Modern Management , July 2023 , pp. 655 - 678
Copyright
© The Author(s), 2022. Published by Cambridge University Press in association with the Australian and New Zealand Academy of Management

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References

Aghalari, A., Nur, F., & Marufuzzaman, M. (2020). A Bender's based nested decomposition algorithm to solve a stochastic inland waterway port management problem considering perishable product. International Journal of Production Economics, 229, 107863.CrossRefGoogle Scholar
Ahmad, H., Ahmad, A., & Ahmad, S. (2018). Efficient energy management in a microgrid. Paper presented at the 2018 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET).CrossRefGoogle Scholar
Ahmad, S., Alhaisoni, M. M., Naeem, M., Ahmad, A., & Altaf, M. (2020). Joint energy management and energy trading in residential microgrid system. IEEE Access, 8, 123334–123346.CrossRefGoogle Scholar
Ahmad, S., Naeem, M., & Ahmad, A. (2019). Low complexity approach for energy management in residential buildings. International Transactions on Electrical Energy Systems, 29(1), e2680.CrossRefGoogle Scholar
Alazba, A., & Aljamaan, H. (2021). Code smell detection using feature selection and stacking ensemble: An empirical investigation. Information and Software Technology, 138, 106648.CrossRefGoogle Scholar
Artamonov, A., Ionkina, K., Tretyakov, E., & Timofeev, A. (2018). Electronic document processing operating map development for the implementation of the data management system in a scientific organization. Procedia Computer Science, 145, 248–253.CrossRefGoogle Scholar
Ban, Y., Liu, M., Wu, P., Yang, B., Liu, S., Yin, L., & Zheng, W. (2022). Depth estimation method for monocular camera defocus images in microscopic scenes. Electronics, 11(13), 2012.CrossRefGoogle Scholar
Boyd, S., Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. USA: Cambridge University Press.CrossRefGoogle Scholar
Chawla, A., Singh, A., Lamba, A., Gangwani, N., & Soni, U. (2019). Demand forecasting using artificial neural networks – a case study of American retail corporation. In Kacprzyk, J. (Ed.), Applications of artificial intelligence techniques in engineering (pp. 79–89). Singapore: Springer.CrossRefGoogle Scholar
Chen, G. (2021). Research on Modern Construction Cost Economic Management Based on the Whole Process of Genetic Algorithm. Paper presented at the Journal of Physics: Conference Series.Google Scholar
Cheung, B. K. S. (2005). Genetic algorithm and other meta-heuristics: Essential tools for solving modern supply chain management. In Chan, C.-K., & Lee, H. W. J. (Eds.), Successful strategies in supply chain management (pp. 144–173). Hong Kong: IGI Global.CrossRefGoogle Scholar
Ding, N., Prasad, K., & Lie, T. T. (2021). Design of a hybrid energy management system using designed rule-based control strategy and genetic algorithm for the series-parallel plug-in hybrid electric vehicle. International Journal of Energy Research, 45(2), 1627–1644.CrossRefGoogle Scholar
Dkhili, N., Eynard, J., Thil, S., & Grieu, S. (2020). A survey of modelling and smart management tools for power grids with prolific distributed generation. Sustainable Energy, Grids and Networks, 21, 100284.CrossRefGoogle Scholar
Doewes, R. I., Gharibian, G., Zadeh, F. A., Zaman, B. A., Vahdat, S., & Akhavan-Sigari, R. (2022). An updated systematic review on the effects of aerobic exercise on human blood lipid profile. Current Problems in Cardiology, 48, 101108. https://doi.org/10.1016/j.cpcardiol.2022.101108.CrossRefGoogle ScholarPubMed
Domanal, S. G., Guddeti, R. M. R., & Buyya, R. (2017). A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment. IEEE Transactions on Services Computing, 13(1), 3–15.CrossRefGoogle Scholar
Esmailiyan, M., Amerizadeh, A., Vahdat, S., Ghodsi, M., Doewes, R. I., & Sundram, Y. (2021). Effect of different types of aerobic exercise on individuals with and without hypertension: An updated systematic review. Current Problems in Cardiology, 48, 101034. https://doi.org/10.1016/j.cpcardiol.2021.101034.CrossRefGoogle ScholarPubMed
Ghasemi, A., Shayeghi, H., Moradzadeh, M., & Nooshyar, M. (2016). A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management. Applied Energy, 177, 40–59.CrossRefGoogle Scholar
Gorska–Rozej, M. K., & Rozej, M. A. (n.d.). Theory and Practice of Modern Management Concepts In Public Organizations. Ročník IX. ČÍSLO 2/2014, 62.Google Scholar
Guan, X., Xu, Z., & Jia, Q.-S. (2010). Energy-efficient buildings facilitated by microgrid. IEEE Transactions on Smart Grid, 1(3), 243–252.CrossRefGoogle Scholar
Gupta, S., Kumar, S., Singh, S. K., Foropon, C., & Chandra, C. (2018). Role of cloud ERP on the performance of an organization: Contingent resource-based view perspective. The International Journal of Logistics Management, 29(2), 659–675.CrossRefGoogle Scholar
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.CrossRefGoogle Scholar
Hossain, M. A., Pota, H. R., Squartini, S., & Abdou, A. F. (2019). Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renewable Energy, 136, 746–757.CrossRefGoogle Scholar
Igiri, C. P., Singh, Y., Bhargava, D., & Shikaa, S. (2020). Improved African buffalo optimisation algorithm for petroleum product supply chain management. International Journal of Grid and Utility Computing, 11(6), 769–779.CrossRefGoogle Scholar
Ilic, B., Djukic, G., & Balaban, M. (2019). Modern management and innovative organizations. Economic and Social Development: Book of Proceedings, 35–43.Google Scholar
Jomthanachai, S., Rattanamanee, W., Sinthavalai, R., & Wong, W.-P. (2020). The application of genetic algorithm and data analytics for total resource management at the firm level. Resources, Conservation and Recycling, 161, 104985.CrossRefGoogle Scholar
Kareska, K. (2016). Challenges in modern management and modern business that Macedonian organizations face in gaining competitive advantage. Journal of Economics, 1(2).Google Scholar
Katyara, S., Shaikh, M. F., Shaikh, S., Khand, Z. H., Staszewski, L., Bhan, V., … Zbigniew, L. (2021). Leveraging a genetic algorithm for the optimal placement of distributed generation and the need for energy management strategies using a fuzzy inference system. Electronics, 10(2), 172.CrossRefGoogle Scholar
Khan, J. A., Qureshi, H. K., & Iqbal, A. (2015). Energy management in wireless sensor networks: A survey. Computers & Electrical Engineering, 41, 159–176.CrossRefGoogle Scholar
Khattar, N., Sidhu, J., & Singh, J. (2019). Toward energy-efficient cloud computing: A survey of dynamic power management and heuristics-based optimization techniques. The Journal of Supercomputing, 75(8), 4750–4810.CrossRefGoogle Scholar
Li, H., & Li, T. (2019). Bark beetle larval dynamics carved in the egg gallery: A study of mathematically reconstructing bark beetle tunnel maps. Advances in Difference Equations, 2019(1), 1–16.CrossRefGoogle Scholar
Li, H., Liu, X., Huang, Z., Zeng, C., Zou, P., Chu, Z., & Yi, J. (2020a). Newly emerging nature-inspired optimization-algorithm review, unified framework, evaluation, and behavioural parameter optimization. IEEE Access, 8, 72620–72649.CrossRefGoogle Scholar
Li, S., Huang, Z., Han, L., & Jiang, C. (2018). A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud. Concurrency and Computation: Practice and Experience, 30(23), e4844.CrossRefGoogle Scholar
Li, W., Zhou, Q., Ren, J., & Spector, S. (2020b). Data mining optimization model for financial management information system based on improved genetic algorithm. Information Systems and e-Business Management, 18(4), 747–765.CrossRefGoogle Scholar
Li, Y., Gao, D. W., Gao, W., Zhang, H., & Zhou, J. (2020a). A distributed double-Newton descent algorithm for cooperative energy management of multiple energy bodies in energy internet. IEEE Transactions on Industrial Informatics, 17(9), 5993–6003.CrossRefGoogle Scholar
Li, Y., Gao, D. W., Gao, W., Zhang, H., & Zhou, J. (2020b). Double-mode energy management for multi-energy system via distributed dynamic event-triggered Newton-Raphson algorithm. IEEE Transactions on Smart Grid, 11(6), 5339–5356.CrossRefGoogle Scholar
Liang, X., Luo, L., Hu, S., & Li, Y. (2022). Mapping the knowledge frontiers and evolution of decision making based on agent-based modeling. Knowledge-Based Systems, 250, 108982.CrossRefGoogle Scholar
Lu, C., Liu, Q., Zhang, B., & Yin, L. (2022). A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop. Expert Systems with Applications, 204, 117555.CrossRefGoogle Scholar
Lü, X., Wu, Y., Lian, J., Zhang, Y., Chen, C., Wang, P., & Meng, L. (2020). Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm. Energy Conversion and Management, 205, 112474.CrossRefGoogle Scholar
Maddouri, M., Elkhorchani, H., & Grayaa, K. (2020). Game theory and hybrid genetic algorithm for energy management and real-time pricing in smart grid: The Tunisian case. International Journal of Green Energy, 17(12), 816–826.CrossRefGoogle Scholar
Maestrini, V., Luzzini, D., Maccarrone, P., & Caniato, F. (2017). Supply chain performance measurement systems: A systematic review and research agenda. International Journal of Production Economics, 183, 315–299.CrossRefGoogle Scholar
Malik, S. A., Gondal, T. M., Ahmad, S., Adil, M., & Qureshi, R. (2019). Towards optimization approaches in smart grid a review. Paper presented at the 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET).CrossRefGoogle Scholar
Nilsson, J., Bernhardsson, B., & Wittenmark, B. (1998). Stochastic analysis and control of real-time systems with random time delays. Automatica, 34(1), 57–64.CrossRefGoogle Scholar
Noh, J., Park, H.-J., Kim, J. S., & Hwang, S.-J. (2020). Gated recurrent unit with genetic algorithm for product demand forecasting in supply chain management. Mathematics, 8(4), 565.CrossRefGoogle Scholar
Paliwal, P., Patidar, N. P., & Nema, R. K. (2014). Planning of grid integrated distributed generators: A review of technology, objectives and techniques. Renewable and Sustainable Energy Reviews, 40, 557–570.CrossRefGoogle Scholar
Rafique, H., Shah, M. A., Islam, S. U., Maqsood, T., Khan, S., & Maple, C. (2019). A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing. IEEE Access, 7, 115760–115773.CrossRefGoogle Scholar
Reddy, K. H. K., Luhach, A. K., Pradhan, B., Dash, J. K., & Roy, D. S. (2020). A genetic algorithm for energy efficient fog layer resource management in context-aware smart cities. Sustainable Cities and Society, 63, 102428.CrossRefGoogle Scholar
Rezaei, E., Paydar, M. M., & Safaei, A. S. (2020). Implementation of accelerating benders decomposition algorithm for supply chain considering new product development and customer relationship management. Journal of Industrial Management Perspective, 10(1, Spring 2020), 41–63.Google Scholar
Rostami, A., Paydar, M. M., & Asadi-Gangraj, E. (2020). A hybrid genetic algorithm for integrating virtual cellular manufacturing with supply chain management considering new product development. Computers & Industrial Engineering, 145, 106565.CrossRefGoogle Scholar
Sarkar, D. (2018). Hybrid approach for resource optimization and management of bridge projects using bootstrap technique and genetic algorithm. International Journal of Construction Management, 18(3), 207–220.CrossRefGoogle Scholar
Serdyuk, V., Serdyuk, T., & Franishyna, S. (2019). Modern management tools for increase energy efficiency level. In Bezpartochnyi, M. & Britchenko, I. (Eds.), Conceptual aspects management of competitiveness the economic entities (vol. 1, chap. 3, pp. 140–148). Przeworsk, Poland: Higher School of Social and Economic.Google Scholar
Sheng, H., Cong, R., Yang, D., Chen, R., Wang, S., & Cui, Z. (2022). UrbanLF: A comprehensive light field dataset for semantic segmentation of urban scenes. IEEE Transactions on Circuits and Systems for Video Technology, 32, 1.CrossRefGoogle Scholar
Tian, Y., Hu, W., Du, B., Hu, S., Nie, C., & Zhang, C. (2019). IQGA: A route selection method based on quantum genetic algorithm-toward urban traffic management under big data environment. World Wide Web, 22(5), 2129–2151.CrossRefGoogle Scholar
Tzanetos, A., & Dounias, G. (2021). Nature inspired optimization algorithms or simply variations of metaheuristics? Artificial Intelligence Review, 54(3), 1841–1862.CrossRefGoogle Scholar
Vahdat, S., & Shahidi, S. (2020). D-dimer levels in chronic kidney illness: A comprehensive and systematic literature review. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 90, 1–18.Google Scholar
Vasudevan, M., Tian, Y.-C., Tang, M., Kozan, E., & Zhang, X. (2018). Energy-efficient application assignment in profile-based data center management through a repairing genetic algorithm. Applied Soft Computing, 67, 399–408.CrossRefGoogle Scholar
Wang, J., Feng, L., Xue, W., & Song, Z. (2011). A survey on energy-efficient data management. ACM SIGMOD Record, 40(2), 17–23.CrossRefGoogle Scholar
Wu, X., Zheng, W., Chen, X., Zhao, Y., Yu, T., & Mu, D. (2021a). Improving high-impact bug report prediction with combination of interactive machine learning and active learning. Information and Software Technology, 133, 106530.CrossRefGoogle Scholar
Wu, X., Zheng, W., Xia, X., & Lo, D. (2021b). Data quality matters: A case study on data label correctness for security bug report prediction. IEEE Transactions on Software Engineering, 48, 2541–2556.CrossRefGoogle Scholar
Xie, Y., Sheng, Y., Qiu, M., & Gui, F. (2022). An adaptive decoding biased random key genetic algorithm for cloud workflow scheduling. Engineering Applications of Artificial Intelligence, 112, 104879.CrossRefGoogle Scholar
Xu, L., Liu, X., Tong, D., Liu, Z., Yin, L., & Zheng, W. (2022). Forecasting urban land use change based on cellular automata and the PLUS model. Land, 11(5), 652.CrossRefGoogle Scholar
Yang, S., Wang, J., Deng, B., Azghadi, M. R., & Linares-Barranco, B. (2021a). Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Transactions on Neural Networks and Learning Systems, 32, 1–15.CrossRefGoogle Scholar
Yang, W., Chen, X., Xiong, Z., Xu, Z., Liu, G., & Zhang, X. (2021b). A privacy-preserving aggregation scheme based on negative survey for vehicle fuel consumption data. Information Sciences, 570, 526–544.CrossRefGoogle Scholar
Yang, X.-S. (2020a). Nature-inspired optimization algorithms. Academic Press.Google Scholar
Yang, X.-S. (2020b). Nature-inspired optimization algorithms: Challenges and open problems. Journal of Computational Science, 46, 101104.CrossRefGoogle Scholar
Yaqub, R., Ahmad, S., Ahmad, A., & Amin, M. (2016). Smart energy-consumption management system considering consumers’ spending goals (SEMS-CCSG). International Transactions on Electrical Energy Systems, 26(7), 1570–1584.CrossRefGoogle Scholar
Yu, Y., Li, M., Li, X., Zhao, J. L., & Zhao, D. (2018). Effects of entrepreneurship and IT fashion on SMEs’ transformation toward cloud service through mediation of trust. Information & Management, 55(2), 245–257.CrossRefGoogle Scholar
Zadeh, F. A., Bokov, D. O., Yasin, G., Vahdat, S., & Abbasalizad-Farhangi, M. (2021). Central obesity accelerates leukocyte telomere length (LTL) shortening in apparently healthy adults: A systematic review and meta-analysis. Critical Reviews in Food Science and Nutrition, 61, 1–10.Google Scholar
Zenggang, X., Mingyang, Z., Xuemin, Z., Sanyuan, Z., Fang, X., Xiaochao, Z., … Xiang, L. (2022). Social similarity routing algorithm based on socially aware networks in the big data environment. Journal of Signal Processing Systems, 94, 1–15.CrossRefGoogle Scholar
Zhang, S., Jiao, Y., & Chen, W. (2017). Demand-side management (DSM) in the context of China's on-going power sector reform. Energy Policy, 100, 1–8.CrossRefGoogle Scholar
Zheng, W., Tian, X., Yang, B., Liu, S., Ding, Y., Tian, J., & Yin, L. (2022). A few shot classification methods based on multiscale relational networks. Applied Sciences, 12(8), 4059.CrossRefGoogle Scholar
Zheng, W., & Yin, L. (2022). Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network. PeerJ Computer Science, 8, e908.CrossRefGoogle ScholarPubMed