###### Abstract
As for the algorithm, the early literature used the mathematical programming method [26], which requires many operations to obtain the Plato optimal solution set, leading to its inefficiency; it is easy to fall into the local optimum in such larger problems, including for the CCHP multi-objective optimization problem. Due to the abovementioned limitations and the increasing maturity of evolutionary algorithms, multi-objective optimal algorithms based on evolutionary algorithms have begun to be widely applied to the CCHP multi-objective optimization problem, such as Multi-objective Particle Swarm Optimization (MOPSO) [23], Multi-objective Genetic Algorithm II (MOGA-II) [27], and Preference-Inspired Coevolutionary Algorithm-g (PICEA-g) [25]. Compared with the traditional mathematical programming method, the evolutionary algorithm has high efficiency, good global search ability, and does not easily fall into the local optimum. Compared with those in the abovementioned literature, the problem model studied in this work is more complicated because this study further considered the exchange of electric energy with the main grid, the capacity of energy-storage equipment, and the supply of cold energy. Therefore, this study intended to convert the original multi-objective optimization problem into a single-objective optimization problem by using the Technique for Order Preference by Similarity to and Ideal Solution (TOPSIS) to measure the balance between the relationship of various objectives, thereby reducing the calculation steps and increasing the efficiency of the solution, and by adopting the weights obtained in the literature [28] to adjust the weight of the trade-off relationship between various objectives. This helps enable decision-makers to adjust the weight of each goal flexibly according to the situation and demand of the current conditions in order to achieve the ideal result. In addition, this study adopted simplified swarm optimization (SSO), which is a relatively novel method in the field of evolutionary algorithms with swarm intelligence as the core and which was originally developed by [PERSON] in 2009 [29] to solve the studied problem. In order to enhance the capability of the area search of SSO, this study applied the differential evolution algorithm (DE) as the area search strategy so that SSO has a better chance to escape the local best solution in the iterative process in order to achieve a higher-quality final solution. The contributions and innovations of the manuscript are clarified clearly as follows:  
1. This work studied the CCHP based on Xikou village, Lieyu township, Kinmen county, Taiwan to add to the currently limited discussions on a larger scale of CCHP, such as in the scope of a community, village, or town. 2. This work studied a large-scale CCHP combined with renewable energy generators and considered the parallel connection of the mains, allowing the exchange of power with the main grid. 3. The Technique for Order Preference by Similarity to and Ideal Solution (TOPSIS) was adopted in this study to convert the original multi-objective optimization problem into a single-objective optimization problem to measure the balance between the relationship of various objectives to reduce the calculation steps and increase the efficiency of the solution. 4. The hybrid of the differential evolution (DE) algorithm with the improved SSO algorithm was adopted to improve the efficiency of the solution. The other sections of this study are organized as follows. The model of CCHP is presented in Section 2. Section 3 shows the research methods of this study. Section 4 represents the experimental results and analysis. Finally, the conclusions are indicated in Section 5.