## 2 Literature Review
The optimization results show an increase in exergy efficiency, exergy and environmental impact index as well as a decrease in the unit cost and cooling loads when compared with a similar study. [PERSON] et al. (2019) employed the greywolf multi-objective technique, coupled with an ANN-based procedure for the optimization of a geothermal-operated poly-generation system. Based on the energy, exergy and economic point of views, the study made use of four distinct approaches that involved the optimization of energy efficiency, investment cost, exergy efficiency and levelized cost. The study achieved its optimization objective of maximizing the energy efficiency and exergy efficiency while minimizing the investment and levelized costs. [PERSON] and [PERSON] (2019) remodelled anORC-based power generation system in a bid to reduce the emission of CO\({}_{2}\). This was achieved by incorporating a membrane separator to harness an appreciable amount of the CO\({}_{2}\) into a gasifier. Utilizing the greywolf optimizer, the proposed model yielded relatively lower CO\({}_{2}\) emission rates and higher exergy efficiency and cost when compared with a similar study. Furthermore, [PERSON] and [PERSON] [36] proposed the analysis and multi-objective optimization of a power and freshwater generation system based on the geothermal and gas turbine cycles. The greywolf optimizer was employed to maximize the net power, freshwater production, exergy efficiency and total emission while minimizing the payback period. The conducted sensitivity analysis confirmed that the air-preheater effectiveness on the system performance criteria is predominant. A solar-based system that produces power, cooling capacity, freshwater and hydrogen has been presented by [PERSON], [PERSON] and [PERSON] [37]. A thermodynamic analysis of the proposed model was carried out to ascertain the base conditions of the generated electricity, drinking water, cooling capacity and hydrogen. Thereafter, the greywolf optimizer was applied, using two different scenarios, to optimize the unit cost, exergy efficiency and rate of freshwater production. [PERSON] and [PERSON] [26] developed a hybrid CCHP system to reduce the primary energy consumption, CO\({}_{2}\) emission and cost. The study employed a modified version of the greywolf optimizer that is based on the non-dominated sorting theory, variable detection, memory-based strategy selection and fuzzy theory. The obtained optimization results were validated using the multi-objective particle swarm optimization technique. [PERSON] et al. [38] presented a methanol-fuelled co-generation system consisting of a solid oxide fuel cell (SOFC), heat recovery unit and absorption power cycle (APC). The greywolf multi-objective technique was used to optimize the exergy efficiency and total cost implemented on three different systems, the SOFC, SOFC-ORC and SOFC-APC. The optimization results indicate a better optimal result from the SOFC-APC due to its non-thermal evaporator, condensation process and temperature glide matching. [PERSON] et al. [39] conducted an investigation on the feasibility of a biomass-based co-generation system. The investigations were carried out using four biomass fuels, with the best fuel--municipal solid waste--subsequently becoming the subject of the multi-objective optimization and parametric analysis of the system. Optimum results were generated and these maximized the total cost and minimized the CO\({}_{2}\) emissions. [PERSON], [PERSON] and [PERSON] [40] developed a heliostat solar-based multi-generation system comprising the Brayton cycle, absorption refrigeration cycle, humidification, dehumidification, etc. In addition to the conducted thermodynamic energetic and economic analysis, the greywolf technique was used to compute optimum values for the exergy efficiency, freshwater production rate and unit product cost. Finally, [PERSON] et al. [5] developed a new, solar-based CCHP system and utilized the RSM for the multi-objective optimization of its net power, CO\({}_{2}\) emission and exergy efficiency.