## 2 Related Works
For solving complex problems such as task scheduling problems in cloud computing, some algorithms such as the Differential Evolution Algorithm have been proposed, but this approach does not return the exact optimal solution; rather, it returns a near optimal solution. Thus, a new algorithm called the DMDE (Diversity-Maintained Multi-Trial Vector Differential Evolution Algorithm for Non-Decomposition Large-Scale Global Optimization) Algorithm has been introduced, which uses a six phase strategy to return an optimal solution. This algorithm uses an effective method to alleviate the risk of damage to population density and early convergence, and it mainly focuses on the N-LSGO (Non-Decomposition Large-Scale Global Optimization). The introduced algorithm was evaluated using the benchmark functions such as the CEC-2018, with sizes of values of 30, 50 and 100, and the CEC-2013, with a size of 1000, for the LSGO problem. Furthermore, the introduced algorithm's performance was analysed statistically using the Friedman test, ANOVA, and Wilcoxon-signed-rank sum, and the algorithm was measured by solving the seven well-known real-world difficulties [16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Mapping a large number of tasks onto cloud resources is called workflow scheduling, and this is done in order to enhance the scheduling process. This area, in particular, has attracted the interest of many research scholars who have introduced different algorithms and programs to enhance the scheduling performance in cloud computing, but, due to the huge size of the tasks, the execution of these tasks is expensive and consumes a lot of time. Thus, to address this problem, an extension of the already existing work \"COHA (Cost Optimised Heuristic Algorithm)\" was performed, and a new algorithm proposed to improve the workflow scheduling called MOWOS (Multi-Objective Workflow Optimization Strategy), which decreases the makespan and execution cost. The introduced algorithm is designed in such a way that it makes use of the task splitting technique in order to break larger tasks into smaller sub-tasks, thus resulting in decreased scheduling length. The simulation results obtained suggest that the introduced algorithm can efficiently be used in allocating virtual machines and their deployment, and it is capable of handling the arriving streaming tasks with a random arriving rate. The proposed algorithm was able to reduce makespan by 10%, decrease cost by 8%, and enhance the resource utilization by 53% while allowing all the tasks to meet their deadlines [26]. Large-scale scientific issues are easily designated for execution in the cloud environment due to the ongoing development of cloud computing. The scientific workflow is designed in such a way that it requires the effective utilization of resources present in the cloud environment to satisfy the user demands and requirements, but the process of scheduling these scientific workflows is a challenge in the cloud. This particular problem is considered an NP-hard problem. Certain restrictions, such as the quality of service, dependencies between tasks, user deadlines, and heterogeneous environments, make it hard for the workflow scheduler to completely utilize the resources available in the cloud. Thus, this paper presents a new multi-objective scheduling algorithm for scientific workflow scheduling in the cloud environment. The introduced algorithm is based on a genetic algorithm that targets cost, makespan, and load-balancing. The algorithm is designed in such a way that it first finds the best result for each of the parameters and, based on those results, the algorithm finds the super best result for all the parameters. The introduced algorithm was compared with standard genetic algorithms such as the PSO (Particle Swarm Optimization) Algorithm, the GA (Genetic Algorithm), and the GA-PSO (a combination of both the Genetic Algorithm and the Particle Swarm Optimization Algorithm).