A Fuzzy MARCOS-Based Analysis of Dragonfly Algorithm Variants in Industrial Optimization Problems

Kalita, Kanak; Ganesh, Narayanan; Shankar, Rajendran; Chakraborty, Shankar

doi:10.15388/23-INFOR538

Informatica

A Fuzzy MARCOS-Based Analysis of Dragonfly Algorithm Variants in Industrial Optimization Problems

Volume 35, Issue 1 (2024), pp. 155–178

Kanak Kalita Narayanan Ganesh Rajendran Shankar Shankar Chakraborty

https://doi.org/10.15388/23-INFOR538

Pub. online: 20 November 2023 Type: Research Article

Open 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.

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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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