## 2 Literature Review
For the solution to nonlinear CHP, the economic dispatch group search method was suggested, which is based on opposition [19]. The foundation of GSA is the gravitational law, which helps particles move in the search space used to solve CHPED [20]. To solve the CHPED problem with various constraints, biogeography-based particle swarm optimization was suggested. In this PSO, particles update their position by using the migration operator [21]. Based on the cuckoo bird's reproduction behavior, CSA techniques were proposed for the CHPED with a valve-point loading problem solution [22]. The CPH problem was solved using a hybrid approach that combined PPS and CSO for local and global search, respectively [23]. Article [24] demonstrates the use of the group search optimizer for CHP dispatch problems. However, a hybrid (TVAC-GSA-PSO) method was used to solve the large-scale, complex CHPED problems [25]. One more hybrid method (bat algorithm + artificial bee colony) with a chaotic-based self-adaptive search strategy known as CSA-BA-ABC was suggested in article [26] to solve the large-scale, non-differential CHP economic dispatch. The suggested FS technique in Article [27] was used to solve the CHP economic dispatch in such a way that the cost should be low and fulfill the constraints. Optimization techniques based on the Kho-Kho game (a game played between two teams) were proposed for the CHPED problem [28]. The CHPED problem was solved for a large data system (48 units) with a minimum total operation cost [29]. To minimize the overall fuel cost of cogeneration units, an improved marine predator optimization algorithm was used [30]. For the solution to the CHP dispatch problem with valve-point loading effects and prohibited operating zones, a wavelet-mutated slime mold technique was used [31]. For the purpose of calculating the system-wide additional costs associated with optimum dispatch using the search optimization approach, an explicit formula was created [32]. For the operation of CHP, a demand response algorithm was used [33]. The heat transfer search technique, which follows the laws of thermodynamics and heat transfer, was used to find the solution to complicated CHP economic dispatch problems [34]. To address the CHP dynamic economic dispatch, a new differential evolution method that has an attractive component and gives mutant vectors more possibilities to find prospective locations utilizing migrating variables was proposed [35]. The TVAC-PSO was suggested to address the multi-objective CHPEED and dynamic economic emission dispatch challenges in the context of operational limitations [36]. It was suggested to combine particle swarm optimization algorithms with enthusiasm-aided teaching and learning-based optimization algorithms to simultaneously reduce overall generation costs while taking constraints into account [37]. For handling a very large CHPED (140 bus system), an article proposed a multiobjective technique that was based on a chaotic opposition-based strategy [38]. The usefulness of the group search method in solving the CHPED issue was reported in an article [39]. For the CHPED problem, a genetic algorithm method was suggested [40]. An efficient tool called the search algorithm was proposed to solve the CHPED with ramp rate constraints [41]. The authors of [42] suggested a cuckoo optimization algorithm to solve the CHP in such a way that energy production costs are minimized. In order to solve the CHPED problem, a paper proposed an IDE approach that makes use of mutation operators, dynamical crossover, and population randomization [43]. To solve CHP dispatch problems with bounded and feasible operating regions, researchers used a TLBO approach. In this method, an opposition-based learning approach was incorporated so that convergence speed was enhanced and the simulation results were improved [44]. Grey wolf optimization techniques were used for the solution of CHPD.