Mohammad AL-Fayoumi

Work place: Dean of Scientific Research & Graduate Studies, AL_ISRA University, Amman, Jordan



Research Interests: Artificial Intelligence, Autonomic Computing, Computer Architecture and Organization, Network Architecture, Computing Platform


Mohammad Al-Fayoumi, male, received his B.S. degree in 1974. In 1977, he earned his Master degree in mathematics, and a postgraduate diploma in computer science was received in 1979. A Ph.D. was received in 1982 in the area of computing science. The last two degrees were awarded from Bucharest University; he joined the Yarmouk University, 1982 in Jordan, as an assistant professor and a head of computer science department. In 1986 he moved to collage of business studies in Kuwait and then moved back to Jordan in Applied Science University as associate professor and a head of computer science department. In 2005 he moved to the Middle East University for Graduate Studies in Jordan as an associate professor and a dean of information technology faculty, he promoted to a professor rank in August, 2008. Then, he is a professor and advisor of graduate studies at the King Abdulaziz University, Saudi Arabia. Currently he is working as Dean of Scientific Research & Graduate Studies AL_ISRA University Amman, Jordan. His research interests include areas of information security, computer simulation, systems development, e-commerce, e-learning and internet security and algorithm analyzes and design. He has supervised many PhD's and master’s degrees research thesis and projects of diverse areas. He has published more than 40 research papers in a multitude of international journals and conferences, in addition to a nine books in the area of computer sciences.

Author Articles
Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem

By Sudip Kumar Sahana Mohammad AL-Fayoumi Prabhat Kumar Mahanti

DOI:, Pub. Date: 8 Apr. 2016

It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO) algorithm which is based on Ant Colony System (ACS) with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO) shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.

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