Global best-guided oppositional algorithm for solving multidimensional optimization problems

  • Mert Sinan TurgutEmail author
  • Oguz Emrah Turgut
Original Article


This paper presents an alternative optimization algorithm to the literature optimizers by introducing global best-guided oppositional-based learning method. The procedure at hand uses the active and recent manipulation schemes of oppositional learning procedure by applying some modifications to them. The first part of the algorithm deals with searching the optimum solution around the current best solution by means of the ensemble learning-based strategy through which unfeasible and semi-optimum solutions have been straightforwardly eliminated. The second part of the algorithm benefits the useful merits of the quasi-oppositional learning strategy to not only improve the solution diversity but also enhance the convergence speed of the whole algorithm. A set of 22 optimization benchmark functions have been solved and corresponding results have been compared with the outcomes of the well-known literature optimization algorithms. Then, a bunch of parameter estimation problem consisting of hard-to-solve real world applications has been analyzed by the proposed method. Following that, eight widely applied constrained benchmark problems along with well-designed 12 constrained test cases proposed in CEC 2006 session have been solved and evaluated in terms of statistical analysis. Finally, a heat exchanger design problem taken from literature study has been solved through the proposed algorithm and respective solutions have been benchmarked against the prevalent optimization algorithms. Comparison results show that optimization procedure dealt with in this study is capable of achieving the utmost performance in solving multidimensional optimization algorithms.


Heat exchanger design Multidimensional optimization Oppositional-based learning Parameter estimation Stochastic search 


Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Faculty of EngineeringEge UniversityBornovaTurkey
  2. 2.Department of Mechanical Engineering, Faculty of EngineeringBakircay UniversityMenemenTurkey

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