An improved chaotic ideal gas molecular movement algorithm for engineering optimization problems. Issue 4 (9th December 2021)

• Record Type: Journal Article
• Authors: Varaee, Hesam; Ghasemi, Mohammad Reza
• Other Names: Chang Victor guestEditor.; Ramachandran Muthu guestEditor.; Li Chung‐Sheng guestEditor.; Zamorano Mariano Rincón guestEditor.; Tomás Rafael Martínez guestEditor.; Vicente José Manuel Ferrández guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-09
• Subjects: chaotic maps -- global optimization -- ideal gas molecular movement -- meta‐heuristic algorithm -- vibrational motion; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12913
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Recently, the ideal gas molecular movement (IGMM) optimization algorithm was introduced by the authors. It is inspired by the movement and collision behaviour of the ideal gas molecules in an isolated medium. Although the IGMM could perform a high potential in determining the global optimum, improving its convergence behaviour came to the attention of the present investigation to deal with any type of especially highly nonlinear optimization problems. For that purpose, two actions took place hybridly. One was the simulation of the vibrational motion of gas molecules, especially at the early stages of the optimization process. The second simultaneous move concerned the non‐repetitive nature of chaotic maps which could diversify the molecules, reduce the threat of premature convergence and improve the convergence speed of the IGMM algorithm. Therefore, this article investigates 10 different chaotic map functions and a random number generator along with four different Vibrational‐based Chaotic IGMM (VCIGMM) strategies to still improve the speed of convergence. The results of applying the proposed algorithm to various numerical and engineering benchmark problems, intensely show that the chaotic maps, merged with vibrational motion of gas molecules, significantly improved the performance of the IGMM. It could considerably outperform some of the well‐known meta‐heuristic optimization algorithms in the literature.