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A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems

  • Absalom E. EzugwuEmail author
  • Olawale J. Adeleke
  • Andronicus A. Akinyelu
  • Serestina Viriri
Original Article

Abstract

The field of continuous optimisation has witnessed an explosion of the so-called new or novel metaheuristic algorithms. Though not all of these algorithms are efficient as proclaimed by their inventors, a few of them have proved to be very efficient and thus have become popular tools for solving complex optimisation problems. Therefore, there is a need for a systematic analysis approach to fairly evaluate and compare the results of some of these optimisation algorithms. In this paper, a set of well-known mathematical benchmark functions are compiled to provide an easily accessible collection of standard benchmark test problems for continuous global optimisation. This set of test problems are used to investigate the computational capabilities and the microscopic behaviour of twelve different metaheuristic algorithms. The required number of function evaluations for reaching the best solution and the run-time complexity of the algorithms are compared. Furthermore, statistical tests are conducted to validate the concluding remarks.

Keywords

Metaheuristics Population-based metaheuristics Swarm intelligence Continuous domain optimisation 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of KwaZulu-NatalPietermaritzburgSouth Africa
  2. 2.School of Mathematics, Statistics and Computer ScienceUniversity of Kwazulu-NatalDurbanSouth Africa
  3. 3.Department of Computer Science and InformaticsUniversity of the Free StateBloemfonteinSouth Africa

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