Chaotic Aquila Optimization Algorithm for Solving Phase Equilibrium Problems and Parameter Estimation of Semi-empirical Models

Abstract

This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers. This work employs 25 different chaotic maps under the framework of Aquila Optimizer. It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems, which have yet to be studied in past literature works. It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases. To test the effectivity of this chaotic variant on real-world optimization problems, it is employed on two constrained engineering design problems, and its effectiveness has been verified. Finally, phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method, and respective solutions have been compared with those obtained from state-of-art optimizers. It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems, showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.