## 2 Literature Review  
The struggle to continuously improve CCHP systems with various optimization techniques represents a progressive research trend in the domain of energy conservation/management. Therefore, this section reviews the relevant pieces of literature that seek to optimize certain performance criteria of the solar-based CCHP system. An extensive review revealed that a greater number of researchers employed the genetic algorithm for optimization applications in solar-assisted CCHP systems. [PERSON] et al. (2018) proposed a modified solar-integrated CCHP system and optimized the amount of electricity it generated, its exergy efficiency, and its total cost per unit exergy via the genetic algorithm approach. They also carried out a parametric study to ascertain how their decision variables (oil mass ratio, Rankine inlet pressure, temperature, etc.) affect the objective functions. The proposed approach improved results in terms of the above mentioned performance criteria thus outperforming conventional methods. The thermodynamic analysis and performance optimization of a solar energy- and natural-gas-integrated CCHP system has been presented by [PERSON] et al. (2019). They employed the genetic optimization algorithm with the purpose of maximizing the energetic and exergetic capacities of the CCHP system. Furthermore, a multi-objective optimization model via a genetic algorithm has been developed by [PERSON] et al. (2019), who proposed an operational flexibility approach determined by the sizes of the photovoltaic (PV) solar panels and gas turbine to improve the CCHP system's energy savings, cost savings, CO\({}_{2}\) emission and grid integration level. The results obtained illustrate that, although the operational flexibility, as selected by the entropy weighting method, improved the system's ability to adjust to variable conditions, corresponding decreases in grid interaction level and exergetic, economic and environmental factors were recorded. [PERSON], [PERSON] and [PERSON] (2019) employed the NSGA-II for the multi-objective optimization of a solar-based CCHP system modelled on three operational modes. Utilizing the gas turbine capacity, PV area and solar collector area as decision variables, an optimal solution that maximized the cost saving and energy saving ratio was obtained. The study confirmed that the CCHP system was significantly affected by energy prices and by the efficiencies of the PV, solar collector and gas turbine. The NSGA-II optimization approach was presented by [PERSON], [PERSON] and [PERSON] (2019), with the aim of achieving the best microgrid capacities necessary to provide the needed tri-generation loads for a specified structure. They compared the results obtained from an internal combustion engine-based CCHP system and a solar energy-integrated CCHP system. This revealed that the latter had a better performance in terms of primary energy saving and CO\({}_{2}\) emission, though at the expense of a slightly increased net present cost. The application of the multi-objective greywolf technique has been employed for the optimization of various multi-generation systems. [PERSON] et al. (2019) proposed a new solar-assisted CCHP system utilizing the heliostat generation unit and employed the RSM and the greywolf optimizer for the multi-objective optimization of exergy performance and unit cost via six selected decision variables. They utilized the three weight-based methods to determine the optimal exergy efficiency, unit cost and performance coefficient. [PERSON] et al. (2019) proposed a heliostat solar-based CCHP system incorporated with a phase change material to regulate the heat rate, thus ensuring a constant temperature input to the gas turbine. They employed the multi-objective greywolf optimization in a bid to further increase the exergy efficiency and power generated while reducing the unit product cost.