## 2 Literature Review  
Researchers have been interested in the CHP economic dispatch problem recently, and the answer has been found in earlier literature utilizing a variety of conventional and modern heuristic methods. In the early 1990s, research started in the field of CHP problems and suggested a quadratic programming method for the solution of this problem for 15 generating unit systems [1]. The Lagrangian relaxation technique was proposed for the solution to the CPH problem. They considered one case study for four generating units with load balance and power generation limits [2]. A classical method called Benders decomposition is used for the solution in cases of four and five generating units with two and three co-generation systems with inequality constraints [3]. To obtain optimum results, the four generating units (two co-generation and one heat unit) were optimized by improved PSO (SPSO) for the load demand of 200 MW and the heat demand of 115 MWth [4]. A novel bee colony optimization algorithm [5] and AI (artificial immune) systems [6] were suggested for the study of a 4-generating unit system of CHPED for the load demand of 600 MW and 150 MWth. Similarly, the firefly algorithm was used for the optimization of CHED for the four generating units, where units 2 and 3 have co-generation and unit 4 has heat. The proposed optimization is used to obtain the global results of power and heat demand of 200 MW and 115 MWth, respectively [7]. For the test data of single heat area and power area systems with loads and heat demands of 200 MW and 115 MWth, respectively, MADS-PSO and, for various power and heat demands, MADS-DACE and MADS-DACE were used [8]. For the solution of nonlinear CHPED, they suggested and demonstrated the effectiveness of TVAC-PSO. The proposed technique was tested on two case study data sets. In the first test case, they considered a four-unit system for the power and heat demand of 200 MW and 115 MWth, respectively. In the second case, they considered a five-unit system for three different load conditions [9]. For the optimization of a large unit data set of CHPED along with constraints, criss-cross optimization was used, and it was found that the proposed techniques gave a global solution for such a large data set. They solved six different cases, and in all cases, the proposed techniques gave the best-optimized solutions compared to the other algorithms [10]. Five different cases of CHPED were considered and global solutions were obtained using the exchange market algorithm. This algorithm was tested for different loads in different data sets (small and large data), and it was found that it gave the global solution in all considered test data [11]. Based on the behavior of humpback whales, the WOA optimization used for the solution of CHPED considered test cases of 24, 84, and 96 data points. The WOA performs well for non-convex nonlinear optimization problems [12]. A crossover and mutation-based improved GA was given for the solution to the CHPED problems [13]. For the non-linear combination of heat and power dispatch systems, the AMPSO algorithm was proposed. To improve the efficiency of the proposed technique, the Taguchi approach was used [14]. A hybrid algorithm is suggested by article [15] for solving the CHP economic emission dispatch problem in such a way as to reduce the cost of generation and emission. Similarly, a deep study discusses the different optimization techniques recommended by the researchers for the solution of the CHP economic emission dispatch problem in article [16]. The CHP economic dispatch problem was solved using the HBOA optimization algorithm, which is based on the interaction of coworkers and employees [17]. A combination of HBA and JSA, commonly known as HBJSA, was used for the solution of the CHP economic load dispatch. The proposed methods overcame the problems associated with HBA and JSA, easily handled the constraints, and solved the nonlinear CHP problem [18].