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A Fuzzy MARCOS-Based Analysis of Dragonfly Algorithm Variants in Industrial Optimization Problems
Volume 35, Issue 1 (2024), pp. 155–178
Pub. online: 20 November 2023      Type: Research Article      Open accessOpen Access
Received
1 December 2022
Accepted
1 November 2023
Published
20 November 2023

Abstract

Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman’s rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration.

References

 
Abedi, M., Gharehchopogh, F.S. (2020). An improved opposition based learning firefly algorithm with dragonfly algorithm for solving continuous optimization problems. Intelligent Data Analysis, 24(2), 309–338.
 
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., Gandomi, A.H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.
 
Badi, I., Muhammad, L., Abubakar, M., Bakır, M. (2022). Measuring sustainability performance indicators using FUCOM-MARCOS methods. Operational Research in Engineering Sciences: Theory and Applications, 5(2), 99–116.
 
Bakır, M., Atalık, Ö. (2021). Application of fuzzy AHP and fuzzy MARCOS approach for the evaluation of e-service quality in the airline industry. Decision Making: Applications in Management and Engineering, 4(1), 127–152.
 
Bakır, M., Akan, Ş., Özdemir, E. (2021). Regional aircraft selection with fuzzy PIPRECIA and fuzzy MARCOS: A case study of the Turkish airline industry. Facta Universitatis, Series: Mechanical Engineering, 19(3), 423–445.
 
Biswal, S., Sahoo, B.B., Jeet, S., Barua, A., Kumari, K., Naik, B., Pradhan, S. (2023). Experimental investigation based on MCDM optimization of electrical discharge machined Al-WC–B4C hybrid composite using Taguchi-MARCOS method. Materials Today: Proceedings, 74, 587–594.
 
Blum, C., Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys (CSUR), 35(3), 268–308.
 
Can, U., Alatas, B. (2017). Performance comparisons of current metaheuristic algorithms on unconstrained optimization problems. Periodicals of Engineering and Natural Sciences, 5(3), 328–340.
 
Chakraborty, S., Chattopadhyay, R., Chakraborty, S. (2020). An integrated D-MARCOS method for supplier selection in an iron and steel industry. Decision Making: Applications in Management and Engineering, 3(2), 49–69.
 
Cheng, L., Wu, X.-h., Wang, Y. (2018). Artificial flora (AF) optimization algorithm. Applied Sciences, 8(3), 329.
 
Debnath, S., Baishya, S., Sen, D., Arif, W. (2021). A hybrid memory-based dragonfly algorithm with differential evolution for engineering application. Engineering with Computers, 37, 2775–2802.
 
Deveci, M., Özcan, E., John, R., Pamucar, D., Karaman, H. (2021). Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS. Applied Soft Computing, 109, 107532.
 
Dhiman, G., Kumar, V. (2019). Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169–196.
 
Faramarzi, A., Heidarinejad, M., Mirjalili, S., Gandomi, A.H. (2020). Marine predators algorithm: a nature-inspired metaheuristic. Expert Systems with Applications, 152, 113377.
 
Floudas, C., Ciric, A. (1989). Strategies for overcoming uncertainties in heat exchanger network synthesis. Computers & Chemical Engineering, 13(10), 1133–1152.
 
Floudas, C.A., Pardalos, P.M. (1990). A Collection of Test Problems for Constrained Global Optimization Algorithms. Springer.
 
Ghanem, W.A., Jantan, A. (2018). A cognitively inspired hybridization of artificial bee colony and dragonfly algorithms for training multi-layer perceptrons. Cognitive Computation, 10, 1096–1134.
 
Holland, J.H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.
 
Jawad, F.K., Mahmood, M., Wang, D., Al-Azzawi, O., Al-Jamely, A. (2021). Heuristic dragonfly algorithm for optimal design of truss structures with discrete variables. Structures, 29, pp. 843–862.
 
Joshi, M., Ghadai, R.K., Madhu, S., Kalita, K., Gao, X.-Z. (2021). Comparison of NSGA-II, MOALO and MODA for multi-objective optimization of micro-machining processes. Materials, 14(17), 5109.
 
Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN’95 – International Conference on Neural Networks, Vol. 4, pp. 1942–1948.
 
Khalilpourazari, S., Khalilpourazary, S. (2020). Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm. Neural Computing and Applications, 32, 3987–3998.
 
Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 200, 671–680.
 
Mafarja, M., Aljarah, I., Heidari, A.A., Faris, H., Fournier-Viger, P., Li, X., Mirjalili, S. (2018). Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems, 161, 185–204.
 
Mirjalili, S. (2015). Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.
 
Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural computing and applications, 27, 1053–1073.
 
Mirjalili, S., Mirjalili, S.M., Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.
 
Mzili, T., Riffi, M.E., Mzili, I., Dhiman, G. (2022). A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem. Decision Making: Applications in Management and Engineering, 5(2), 287–299.
 
Neshat, M., Sepidnam, G., Sargolzaei, M. (2013). Swallow swarm optimization algorithm: a new method to optimization. Neural Computing and Applications, 23(2), 429–454.
 
Reddy, A.S. (2016). Optimization of distribution network reconfiguration using dragonfly algorithm. Journal of Electrical Engineering, 16(4), 10.
 
Ryoo, H.S., Sahinidis, N.V. (1995). Global optimization of nonconvex NLPs and MINLPs with applications in process design. Computers & Chemical Engineering, 19(5), 551–566.
 
Saremi, S., Mirjalili, S., Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30–47.
 
Sauer, R., Colville, A., Burwick, C. (1964). Computer points way to more profits. Hydrocarbon Processing, 84(2).
 
Sayed, G.I., Tharwat, A., Hassanien, A.E. (2019). Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Applied Intelligence, 49, 188–205.
 
Shirani, M.R., Safi-Esfahani, F. (2020). BMDA: applying biogeography-based optimization algorithm and Mexican hat wavelet to improve dragonfly algorithm. Soft Computing, 24(21), 15979–16004.
 
Sörensen, K., Glover, F. (2013). Metaheuristics. Encyclopedia of Operations Research and Management Science, 62, 960–970.
 
Sree Ranjini, K.S., Murugan, S. (2017). Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Systems with Applications, 83, 63–78.
 
Stanković, M., Stević, Ž., Das, D.K., Subotić, M., Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 457.
 
Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: measurement of alternatives and ranking according to Compromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.
 
Xu, J., Yan, F. (2019). Hybrid Nelder–Mead algorithm and dragonfly algorithm for function optimization and the training of a multilayer perceptron. Arabian Journal for Science and Engineering, 44, 3473–3487.
 
Yang, X.-S. (2012). Flower pollination algorithm for global optimization. In: International Conference on Unconventional Computing and Natural Computation, pp. 240–249, Springer.
 
Yu, C., Cai, Z., Ye, X., Wang, M., Zhao, X., Liang, G., Chen, H., Li, C. (2020). Quantum-like mutation-induced dragonfly-inspired optimization approach. Mathematics and Computers in Simulation, 178, 259–289.

Biographies

Kalita Kanak

K. Kalita is an associate professor at the Mechanical Engineering Department at Vel Tech University, India. With a dedicated focus on research, K. Kalita specializes in optimizing composite laminated structures. This specialization is complemented by a strong background in computational mechanics and soft computing techniques, contributing significantly to the field through innovative research and academic excellence.

Ganesh Narayanan

N. Ganesh brings a wealth of experience to his role as a senior associate professor at the School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus. With a career spanning nearly two decades in teaching, training and research, he has established himself as an authority in this field. His research interests are diverse and forward-thinking, encompassing software engineering, agile software development, prediction and optimization techniques, deep learning, image processing and data analytics.

Shankar Rajendran

R. Shankar serves as an associate professor in the Department of Computer Science and Engineering at the Koneru Lakshmaiah Education Foundation in Vaddeswaram, India. His research interests are software development optimization, deep learning and data analytics.

Chakraborty Shankar
s_chakraborty00@yahoo.co.in

S. Chakraborty is a distinguished member of the faculty at Jadavpur University’s Department of Production Engineering in India. Renowned for contributions to academia and research, professor Chakraborty is a regular reviewer for several journals of international repute. The research interests of professor Chakraborty encompass a broad range of topics including operations research, multi-criteria decision making, statistical quality control and soft computing.


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Keywords
Dragonfly algorithm Process optimization MCDM MARCOS

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