## 2 Literature Review  
Various metaheuristics have been proposed for solving NWFSSP for different objective criteria. This section provides a comparative review of various metaheuristics, used by earlier researchers, for solving NWFSSP for TFT as an optimization criterion, along with various hybridization techniques for the improvement of results obtained via metaheuristics over the last decade. [PERSON] and [PERSON] [3] applied different kinds of metaheuristics and constructive heuristics, such as nearest neighbor, cheapest insertion, and the pilot method, along with steepest descent (SD), Iterated Steepest Descent (ISD), Simulated Annealing (SA), and Tabu Search (TS), and examined tradeoffs between solution quality and running time. Implementation efforts showed that high-quality results were obtained in an efficient way by applying metaheuristics. Later, [PERSON] et al. [22] proposed the Hybrid Harmony Search (HHS) algorithm using Nawaz-Enscore-Ham (NEH) [23] heuristics, and provided a solution with an appropriate balance between global exploration and local exploitation. [PERSON] et al. [24] attempted to use the Enhanced Migrating Birds Algorithm (EMBO), based on neighborhood search heuristics, to avoid local optima. In order to improve the quality of solutions, [PERSON] et al. [25] came up with a novel Evolutionary Clustering Search (ECS) metaheuristic approach, and found it to have better results than the method of [PERSON] [3], Discrete Particle Swarm Optimization (DPSO) [9]. Genetic Algorithm (GA) as a metaheuristic technique was quite popular for solving optimization problems, and, later, it was observed that the solution quality was improved using a hybridization technique. [PERSON] and [PERSON] [7] proposed the hybrid genetic algorithm and a novel local search scheme for improving solution qualities. [PERSON] et al. [26] also proposed the Hybrid-Genetic Algorithm (HGA) using a new crossover operator, which helped them to argue that metaheuristics always yield better solutions than heuristics. Further, the Asynchronous Genetic Algorithm (AGA) proposed by [PERSON] et al. [27] provided a solution for avoiding gene diversity in a short amount of time. Recently, [PERSON] et al. [28] used a constraint-simplified mixed integer programming model and proposed the Hybridization of GA with the Neighborhood Search (H&NSGA). Although GA yields better solutions, it requires the appropriate tuning of parameters; thus, a new population-based methodology working on the principle of social behavior was introduced. PSO is a population-based metaheuristic technique that is quite popular nowadays, because of its better solution quality achieved with less parameter tuning efforts. The comparative analysis of various metaheuristics for NWFSSP, by [PERSON] et al. [29; 30], advocated the effectiveness of PSO. Some remarkable metaheuristics were developed by [PERSON] et al. [9; 10] for solving NWFSSP. [PERSON] et al. [9] developed Discrete Particle Swarm Optimization (DPSO) by considering both the makespan and total flow time minimization as optimization criteria for solving the no-wait flowshop scheduling problem. [PERSON] et al. [31] proposed a Hybrid Particle Swarm Optimization (HPSO) algorithm, based on the Memetic Algorithm (MA), and provided better solutions. On the basis of a detailed literature review, and a study of the status of research work related to use of metaheuristics for solving NWFSSP for TFT as optimization criteria, the following research gaps were identified:  
* The published literature, thus far, has primarily addressed permutation-type flow shop scheduling problems, but fewer attempts were found in the study of the \"no-wait\" type variant of flow shop scheduling problems (NWFSSP).