A comparative study of metaheuristics algorithms based on their performance of complex benchmark problems

  • Tithli Sadhu Department of Chemistry, National Institute of Technology Durgapur, West Bengal, India; Department of Biochemistry, School of Agriculture, SR University, Hanumakonda, Telangana, India https://orcid.org/0000-0003-3585-4666
  • Somanth Chowdhury Department of Chemical Engineering, National Institute of Technology Durgapur, West Bengal, India
  • Shubham Mondal Department of Computer Science and Engineering, Institute of Engineering and Management Kolkata, West Bengal, India
  • Jagannath Roy Department of Mathematics, National Institute of Technology Warangal, Telangana, India https://orcid.org/0000-0002-5252-5736
  • Jitamanyu Chakrabarty Department of Chemistry, National Institute of Technology Durgapur, West Bengal, India
  • Sandip Kumar Lahiri Department of Chemical Engineering, National Institute of Technology Durgapur, West Bengal, India
Keywords: Metaheuristic, Algorithms, Optimization, Performance, Benchmark problems


Metaheuristic approaches with extremely important improvements are very promising in the solution of intractable optimization problems. The objective of the present study is to test the capability of applications and compare the performance of the four selected algorithms from “classical” (simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) and “new generation” (firefly algorithm (FFA), krill herd (KH), grey wolf optimization (GWO), and symbiotic organism search (SOS)) each by solving selected benchmark problems that are used in the literature for algorithm testing purpose. The selected test problems had very complex objective functions and associated constraints with multiple local optima. Among all selected algorithms, the “new generation” SOS and KH algorithm successfully solved most of all the selected benchmark problems and achieved the best solution for most of them. Among four “classical” algorithms, DE, and PSO effectively attained the optimal solution which was very close to the best one. However, the “new generation” algorithm performed much better than the “classical” one. Therefore, no firm conclusion can be done about the universally best algorithm and their performance may be varied for different benchmark problems. However, in this study for the seven selected test problems, SOS and KH exhibited the most promising result and great potential with respect to execution time also. This study gives some insights to use SOS and KH as the best-performing algorithms to the novice user who can easily get lost in the plethora of large optimization algorithms.


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How to Cite
Sadhu, T., Chowdhury, S., Shubham Mondal, Jagannath Roy, Chakrabarty, J., & Lahiri, S. K. (2023). A comparative study of metaheuristics algorithms based on their performance of complex benchmark problems. Decision Making: Applications in Management and Engineering, 6(1), 341-364. https://doi.org/10.31181/dmame0306102022r