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| 1 | A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network显示文摘Computer networks and power transmission networks are treated as capacitated flow networks.A capacitated flow network may partially fail due to maintenance.Therefore,the capacity of each edge should be optimally assigned to face critical situations-i.e.,to keep the network functioning normally in the case of failure at one or more edges.The robust design problem(RDP)in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure.The RDP is known as NP-hard.Thus,capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper.The problem is formulated mathematically,and a genetic algorithm is proposed to determine the optimal solution.The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity.Some numerical examples are presented to illustrate the efficiency of the proposed approach. | Ahmed Y.Hamed Monagi H.Alkinani M.R.Hassan | 2020 | Computers, Materials & Continua2020，，9： | 1 |
| 2 | Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms显示文摘Task scheduling is the main problem in cloud computing that reduces system performance;it is an important way to arrange user needs and perform multiple goals.Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation,task scheduling,security,privacy,etc.To improve system performance,an efficient task-scheduling algorithm is required.Existing task-scheduling algorithms focus on task-resource requirements,CPU memory,execution time,and execution cost.In this paper,a task scheduling algorithm based on a Genetic Algorithm(GA)has been presented for assigning and executing different tasks.The proposed algorithm aims to minimize both the completion time and execution cost of tasks and maximize resource utilization.We evaluate our algorithm’s performance by applying it to two examples with a different number of tasks and processors.The first example contains ten tasks and four processors;the computation costs are generated randomly.The last example has eight processors,and the number of tasks ranges from twenty to seventy;the computation cost of each task on different processors is generated randomly.The achieved results show that the proposed approach significantly succeeded in finding the optimal solutions for the three objectives;completion time,execution cost,and resource utilization. | Ahmed Y.Hamed Monagi H.Alkinani | 2021 | Computers, Materials & Continua2021，，12： | 1 |
| 3 | Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems显示文摘Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan. | Ahmed Y.Hamed M.Kh.Elnahary Faisal S.Alsubaei Hamdy H.El-Sayed | 2023 | Computers, Materials & Continua2023，，1： | 1 |
| 4 | Ant Colony Optimization for Multi-Objective Multicast Routing显示文摘In the distributed networks,many applications send information from a source node to multiple destination nodes.To support these applications requirements,the paper presents a multi-objective algorithm based on ant colonies to construct a multicast tree for data transmission in a computer network.The proposed algorithm simultaneously optimizes total weight(cost,delay and hop)of the multicast tree.Experimental results prove the proposed algorithm outperforms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem.Also,it is able to find a better solution with fast convergence speed and high reliability. | Ahmed Y.Hamed Monagi H.Alkinani M.R.Hassan | 2020 | Computers, Materials & Continua2020，，6： | 0 |
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