Coupling a chaotically encoded firefly algorithm with ranking to a physics-based mathematical model for robust optimisation of a gas turbine energy system

The aim of this study is to probe the potentials of a well-known metaheuristic approach called firefly algorithm with ranking (FAR) for optimising the operating parameters of a complex gas turbine energy system. FAR is a modified version of classic firefly algorithm (FA) which is suited for handling complex constraint optimisation problems. Firstly, by using the first law of thermodynamics, a mathematical model is implemented to analyse the most important design parameters affecting the efficiency of the gas turbine energy system. Thereafter, two well-known chaotic maps, i.e., Gauss and sinusoidal maps, are embedded into the algorithmic structure of FAR to prepare a powerful tool for the considered problem. To ascertain the veracity and the efficacy of the proposed chaos-enhanced FAR (CFAR), a number of chaos-enhanced rival modern optimisers, i.e., chaotic artificial bee colony (CABC), chaotic particle swarm optimisation (CPSO), and chaotic genetic algorithm (CGA), are applied to the considered optimisati...

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