## 1 Introduction  
Scheduling is an integral part of advanced manufacturing systems. Production scheduling is the arrangement of jobs to be processed on available machines under some constraints. Flow Shop, Job Shop, and Open Shop are the classical models used to solve scheduling problems. The Flow Shop Scheduling Problem (FSSP) addresses most famous machine scheduling problems of many manufacturing systems, assembly lines, and information service facilities [1; 2]. Sometimes Flow Shops have no delay situations that occur in the production environment in many real-life situations where a job must be processed continuously, without any interruption, from beginning to end, in order to follow the technological order of a process, which leads to a variant with the added constraint of \"no-wait\" [3]. In order to maintain continuous processing of a job in No-Wait Flow Shop Scheduling (NWFSS),the start of a job by the first machine is delayed, if required, and the scheduling of such a \"no-wait\" constraint has attracted many researchers. A No-Wait Flow Shop Scheduling Problem (NWFSSP) has found applications in various processing industries, such as the chemical industry [4], food [5], concrete ware production [6], pharmaceuticals [7], etc. [PERSON] [8] reviewed scheduling problems with the no-wait constraint with respect to different shop environments, performance measures, setup types, and optimal scheduling criteria. Among various optimality criteria, viz. makespan, Total Flow Time (TFT), tardiness, lateness, number of tardy jobs, etc., makespan [9] and TFT [7, 10] are of major interest for solving scheduling problems of no-wait type of flow shops, because makespan and TFT determine the total processing time for an entire pool of jobs and the total processing times for individual jobs respectively. This paper addresses TFT as an objective function for solving NWFSSP. TFT is considered to be an important performance measure that, when optimized, reflects a stable or uniform utilization of resources, a rapid turn-around on jobs, and the minimization of in-process inventory [4]. The main objective of planning a production schedule is to discover the sequence of jobs, which minimizes TFT. The classical brute force method for finding such job sequences fails for large-sized problems, as computational complexity rises exponentially as n!, where \"n\" is number of jobs; thus, NWFSSP is treated as combinatorial optimization problem. Because of computational complexity, researchers [11, 12, 13] have concluded that NWFSSP with more than two machines is NP-hard. The solutions to solve such NP-hard problems consist of an approximate algorithm which uses constructive heuristics, local search methods, and metaheuristics. Generally, heuristic algorithms can obtain near-optimal solutions in an acceptable amount of time. Earlier researchers [14, 15, 16, 17, 18] have developed efficient constructive heuristic algorithms for TFT minimization; however, these attempts are not useful for identifying near optimal solutions for larger-sized problems, as these developed algorithms usually get trapped in local optima for large-problem sizes [15]. Local search methods can find the solutions, but the quality of a solution and computational time depends to a great extent on appropriate initial populations [19]. Due to the advent of computation techniques, metaheuristics can be used to solve problems in less time so that the limitation of computational complexity can be resolved through metaheuristic applications. The field of metaheuristics, for application in combinatorial optimization problems of the scientific and industrial worlds, is growing rapidly [20]; on the other hand, attempts to use metaheuristics for solving combinatorial optimization problems began late. The recent past has witnessed a remarkable shift towards the hybridization of metaheuristics for optimization. The current trend focuses more on problem-specific approaches that lead to hybridization [21]. This paper attempts to use a metaheuristic technique, viz.