Moatamad Mohamed | Aswan University (original) (raw)

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Papers by Moatamad Mohamed

American Journal of Engineering and Applied Sciences, 2020

The robust design in a flow network is one of the most important problems. It is defined as searc... more The robust design in a flow network is one of the most important problems. It is defined as searching the optimal capacity that can be assigned to the nodes such that the network still survived even under the node’s failure. This problem is considered NP-hard. So, this study presents a genetic-based algorithm to determine the maximum node capacity for a two-commodity flow network with node failure. I.e., searching the minimum sum of the assigned capacities and the maximum network reliability. The obtained results show that The proposed GA-based algorithm succeeded to solve the robust problem for the two-commodity flow network considering the node’s failure.

Intelligent Automation & Soft Computing

American Journal of Engineering and Applied Sciences

Computers, Materials & Continua

Journal of Industrial & Management Optimization

International Journal of Computer Science, Engineering and Applications

Indian Journal of Science and Technology

Objectives: The study aims to present a multi objective genetic algorithm in order to solve multi... more Objectives: The study aims to present a multi objective genetic algorithm in order to solve multi-objective components assignment problem subject to lead-time constraints. Methods/Statistical Analysis: The study has used non-dominated sorting genetic algorithm II to solve component assignment problems under total lead-time constraints and determine the most optimal solution characterized by a maximum reliability and minimum total lead-time. The proposed method is tested on different examples from the literature to illustrate its efficiency in comparison with a single genetic algorithm. Findings: The proposed algorithm succeeded in identifying the optimal solution to the presented problem in comparison with the single genetic algorithm without guessing or determining the initial value for the total lead-time. Moreover, similar observation was identified for the six-node network example. However, no comparison for TANET example was present because there is no literature dealt it for the presented problem. The proposed approach succeeded by obtaining the most optimal solution to the presented problem. Application/Improvements: With the help of proposed approach, the system reliability is maximized and total lead-time is minimized. Future researches may focus on other algorithms to improve the reliability and lead-time.

Optimal components assignment problem subject to system reliability, total lead-time, and total c... more Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods

Computer networks and power transmission networks are treated as capacitated flow networks. A cap... more 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.

In the distributed networks, many applications send information from a source node to multiple de... more 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.

This paper presents the identical parallel machine's scheduling problem when the jobs are submitt... more This paper presents the identical parallel machine's scheduling problem when the jobs are submitted over time. This problem consists of assigning N various jobs to M identical parallel machines to reduce the workload imponderables among the different machines. We generalized the mixed-integer linear programming approach to decrease the workload imbalance between the different machines, and that is done by converting the problem to the mathematical model. The studied cases are presented for different problems, and it indicates to an online system, and this system does not know the arrival times of the jobs before and reduce Makespan criterion is not well appropriate to describe the utilization for this online problem. The obtained results proved good solutions for the scheduling problem compared with standard algorithms.

Many real-world networks such as freight, power and long distance transportation networks are rep... more Many real-world networks such as freight, power and long distance transportation networks are represented as multi-source multi-sink stochastic flow network. The objective is to transmit flow successfully between the source and the sink nodes. The reliability of the capacity vector of the assigned components is used an indicator to find the best flow strategy on the network. The Components Assignment Problem (CAP) deals with searching the optimal components to a given network subject to one or more constraints. The CAP in multi-source multi-sink stochastic flow networks with multiple commodities has not yet been discussed, so our paper investigates this scenario to maximize the reliability of the capacity vector subject to an assignment budget. The mathematical formulation of the problem is defined, and a proposed solution based on genetic algorithms is developed consisting of two steps. The first searches the set of components with the minimum cost and the second searches the flow vector of this set of components with maximum reliability. We apply the solution approach to three commonly used examples from the literature with two sets of available components to demonstrate its strong performance.

Many applications require to send information from a source node to multiple destinations nodes. ... more Many applications require to send information from a source node to multiple destinations nodes. To support these applications, the paper presents a multi-objective based genetic algorithm, which is used in the construction of the multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weights (cost, delay, and hop) of the multicast tree. Experimental results prove that the proposed algorithm outper-forms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, the proposed approach has been applied to ten-node and twenty-node network to illustrate its efficiency. In addition, the execution time is reported for each studied case and the obtained results are compared with the results obtained by the previously based ant colony algorithm presented recently to solve the same problem. Finality, summing up the three objectives (cost, delay, and hop) to be one objective called the weight of the tree to speed up the searching process by using the proposed algorithm to find the best solutions.

The reliability of a multi-state network is defined as the probability that the network can succe... more The reliability of a multi-state network is defined as the probability that the network can successfully send d (demand) units of data from the source to the sink. To predict the value of maximum demand (dmax) that could be accommodated by a network, a cut-set based approach is presented in this study. The presented approach is simple and easy to implement. The proposed method was applied to many examples studied in literature to illustrate its efficiency. The results show that the reliability value at maximum demand (max d R) is less than any Rd.

The robust design in a flow network is one of the most important problems. It is defined as searc... more The robust design in a flow network is one of the most important problems. It is defined as searching the optimal capacity that can be assigned to the nodes such that the network still survived even under the node's failure. This problem is considered NP-hard. So, this study presents a genetic-based algorithm to determine the maximum node capacity for a two-commodity flow network with node failure. I.e., searching the minimum sum of the assigned capacities and the maximum network reliability. The obtained results show that The proposed GA-based algorithm succeeded to solve the robust problem for the two-commodity flow network considering the node's failure.

American Journal of Engineering and Applied Sciences, 2020

Intelligent Automation & Soft Computing

American Journal of Engineering and Applied Sciences

Computers, Materials & Continua

Journal of Industrial & Management Optimization

International Journal of Computer Science, Engineering and Applications

Indian Journal of Science and Technology

The robust design in a flow network is one of the most important problems. It is defined as searc... more The robust design in a flow network is one of the most important problems. It is defined as searching the optimal capacity that can be assigned to the nodes such that the network still survived even under the node's failure. This problem is considered NP-hard. So, this study presents a genetic-based algorithm to determine the maximum node capacity for a two-commodity flow network with node failure. I.e., searching the minimum sum of the assigned capacities and the maximum network reliability. The obtained results show that The proposed GA-based algorithm succeeded to solve the robust problem for the two-commodity flow network considering the node's failure.