Title: Real coded self-organising migrating genetic algorithm for nonlinear constrained optimisation problems

Authors: Avijit Duary; Nirmal Kumar; Md. Akhtar; Ali Akbar Shaikh; Asoke Kumar Bhunia

Addresses: Department of Mathematics, Jadavpur University, West Bengal, 700032, India; Department of Mathematics, Supreme Knowledge Foundation Group of Institutions, West Bengal, 712139, India ' Department of Mathematics, The University of Burdwan, West Bengal, 713104, India ' Department of Mathematics, The University of Burdwan, West Bengal, 713104, India ' Department of Mathematics, The University of Burdwan, West Bengal, 713104, India ' Department of Mathematics, The University of Burdwan, West Bengal, 713104, India

Abstract: The objective of this article is to propose a new hybrid algorithm named as real coded self-organising migrating genetic algorithm (C-RCSOMGA) by combining real coded genetic algorithm (RCGA) and modified self-organising migrating algorithm (SOMA) for solving the nonlinear constrained optimisation problems. In RCGA, a modified mutation operator called as double mutation operator has been introduced combining two different existing mutation operators, whereas in SOMA, a modified strategy has been proposed. To test the performance of the proposed algorithm, a set of test problems taken from the existing literature has been solved and the simulated results have been compared numerically as well as graphically with the existing algorithms. In the graphical comparison, a modification of performance index (PI) has been made. Finally, with the help of modified performance index (MPI), it has been shown that the proposed hybrid algorithm has performed much better than the existing algorithms.

Keywords: genetic algorithm; self-organising migrating algorithm; SOMA; performance index; nonlinear constrained optimisation; global optimisation.

DOI: 10.1504/IJOR.2022.125722

International Journal of Operational Research, 2022 Vol.45 No.1, pp.29 - 67

Received: 13 Sep 2019
Accepted: 30 Dec 2019
Published online: 27 Sep 2022 *

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