A Computational Grid (CG) consists of networks that are linked to implementing grid computing. Here, a huge computational task is shared amongst the individual machines, where the calculations are executed in parallel and the final results are combined and returned to the original computer. One of the most critical issues in the CG is to measure the performance, which is handled by the task scheduling algorithm. This algorithm is ultimately responsible for assigning the task to suitable resources like memory, CPU, and so on. Evolutionary Algorithm (EA) is one of the task scheduling algorithms that produce many feasible solutions for the given problem. In this article, the MapReduce framework is implemented using the Pareto Multi-Objective Evolutionary Algorithm (PMOEA) in CG. The Pareto Multi-Objective Evolutionary Algorithm (PMOEA) incorporates other two algorithms such as Pareto Multi-Objective Genetic Algorithm (PMOGA) and the Pareto Multi-Objective Particle Swarm Optimization (PMOPSO) to resolve the research problem effectively. The performance of the PMOEA can be further measured by the performance metrics like Ratio of Non-dominated Individuals (RNI), Maximum Spread (S_max), and Coverage of Two sets (C). The evaluation result indicates that the Non-dominated Sorting Genetic Algorithm (NSGA2-PHC) outperforms all other techniques.

计算网格（CG）由链接到实现网格计算的网络组成。在这里，在各个机器之间共享了巨大的计算任务，在这些机器中并行执行计算，并将最终结果合并并返回到原始计算机。CG中最关键的问题之一是衡量性能，这由任务调度算法处理。该算法最终负责将任务分配给合适的资源，例如内存，CPU等。进化算法（EA）是为给定问题提供许多可行解决方案的任务调度算法之一。在本文中，MapReduce框架是使用CG中的帕累托多目标进化算法（PMOEA）实现的。帕累托多目标进化算法（PMOEA）结合了其他两种算法，例如帕累托多目标遗传算法（PMOGA）和帕累托多目标粒子群优化（PMOPSO），可以有效地解决研究问题。PMOEA的绩效可以通过绩效指标进一步衡量，例如非主导个人比例（RNI），最大利差（S_max）和两组的覆盖率（C）。评估结果表明，非支配排序遗传算法（NSGA2-PHC）优于所有其他技术。