Predicting the chemical equilibrium point of reacting components in gaseous mixtures through a novel Hierarchical Manta-Ray Foraging Optimization Algorithm

This study proposes a Hierarchical Manta-Ray Foraging Optimization (HMRFO) algorithm for calculating the equilibrium points of chemical reactions. To improve the solution diversity in the trial Manta-Ray population and enhance the general optimization effectivity of the algorithm, an ordered hierarchy is integrated into the original algorithm, taking into account the efficient search strategies of Elite-Opposition learning, Dynamic Opposition Learning, and Quantum search operator. Within this proposed concept, the Manta-ray population is divided into three main sub-populations: the Elite Oppositional learning scheme manipulates top elite individuals, Dynamic Oppositional learning search equations update average population members, and quantum-based learning equations process the worst members. The improved MRFO is applied to a hundred 30D and 500D optimization benchmark functions, and results have been compared to those obtained from state-of-art metaheuristic optimizers. Then, the proposed optimizer solved twenty-eight test problems previously employed in CEC-2013 competitions, and corresponding results were benchmarked against well-reputed metaheuristics. This research study also suggests a novel mathematical model for solving chemical equilibrium problems for ideal gas mixtures. Four challenging case studies related to chemical equilibrium problems have been performed by the HMRFO for varying test conditions, and it is observed that HMRFO can effectively cope with the tedious nonlinearities and complexities of the governing thermodynamic models associated with solving chemical equilibrium problems for gaseous reacting mixture components.