## 2 Literature Survey  
[PERSON] and [PERSON] [14] employed an Ant Lion Optimization (ALO) algorithm to maximize resource utilization and minimize makespan in the cloud platforms. In this literature, the traditional ALO algorithm was integrated with elite-based differential evolutions for enhancing an exploration and exploitation ability that avoids the local optima problem. The efficacy of the developed optimization algorithm was analyzed on the real-trace and synthetic databases by utilizing Cloud-Sim. The results demonstrate that the ALO algorithm outperformed the existing optimization algorithms by means of processing time. [PERSON] et al. [15] integrated a greedy strategy with the Genetic Algorithm (GA) for optimizing the scheduling of tasks. The developed algorithm's performance was analyzed using dissimilar performance measures such as QoS parameters, average response time, and total completion time. The results showed that the developed algorithm performs more effectively related to the existing task scheduling algorithms. However, the use of elite-based differential evolutions and greedy strategy did not provide optimal solutions to all issues. In the present scenario, cloud users extensively use cloud-based applications. [PERSON] and [PERSON] [16] have developed a hybrid optimization algorithm, the Ant Colony Optimization (ACO) algorithm with GA, for the effective handling of cloud users' requests. This study utilized a Utility-Based Scheduler (UBS) for identifying suitable resources and order of the tasks. Here, the ACO algorithm was utilized for enhancing the crossover operation in the GA. The extensive experimental investigation stated that the developed hybrid optimization algorithm obtained superior performance in ensuring QoS parameters and task allocation. The efficacy of the hybrid optimization algorithm was validated in light of throughput, completion time, and response time. However, the integration of the two optimization algorithms increased the time complexity. In addition to this, [PERSON] al. [17] presented a PSO algorithm with low cost and time complexity for the effective balancing and scheduling of tasks in cloud computing. However, the conventional PSO algorithm had the problem of a poor convergence rate. [PERSON] and [PERSON] [18] presented a mean Grey Wolf Optimization (GWO) algorithm to reduce makespan and energy consumption in cloud computing. The aims of the presented optimization algorithm were analyzed utilizing Cloud-Sim for standard workloads (right- and left-skewed). The GWO algorithm had high time complexity because it needed to perform four operations (attacking, encircling, judging, and searching for prey) for scheduling the tasks. [PERSON] et al. [19] integrated two meta-heuristic-based optimization algorithms, the PSO algorithm and Multi-Vorse Optimization (MVO) algorithm, for the effective scheduling of tasks in the cloud platforms. The obtained numerical results confirmed the effectiveness of the presented hybrid optimization algorithm, which achieved superior performance in improving resource utilization and reducing makespan time. [PERSON] et al. [20] combined GA with electro search for enhancing task scheduling in cloud platforms by employing different parameters. Here, the electro search provided the best global optimal solutions and the GA provided the best local optimal solutions. As discussed in the earlier literature, the integration of two optimization algorithms increased the time complexity of the system. [PERSON] and [PERSON] [21] integrated two optimization algorithms, such as the PSO algorithm and cuckoo search algorithm, that made cloud-computing services faster. The primary objective of this literature study was to decrease the violation rate and makespan. On the other hand, [PERSON] and [PERSON] [22] implemented a discrete Artificial Bee Colony (ABC) algorithm for flexible task scheduling in the cloud platforms.