Basel A. Mahafzah

Bio: Basel A. Mahafzah is an academic researcher from University of Jordan. The author has contributed to research in topics: Speedup & Interconnection. The author has an hindex of 16, co-authored 36 publications receiving 559 citations.

Sea Lion Optimization Algorithm

Abstract: This paper suggests a new nature inspired metaheuristic optimization algorithm which is called Sea Lion Optimization (SLnO) algorithm. The SLnO algorithm imitates the hunting behavior of sea lions in nature. Moreover, it is inspired by sea lions' whiskers that are used in order to detect the prey. SLnO algorithm is tested with 23 well-known test functions (Benchmarks). Optimization results show that the SLnO algorithm is very competitive compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA) and Dragonfly Algorithm (DA).

Solving traveling salesman problem using parallel repetitive nearest neighbor algorithm on OTIS-Hypercube and OTIS-Mesh optoelectronic architectures

Abstract: Over the past years, researchers drew their attention to propose optoelectronic architectures, including optical transpose interconnection system (OTIS) networks. On the other hand, there are limited attempts devoted to design parallel algorithms for applications that could be mapped on such optoelectronic architectures. Thus, exploiting the attractive features of OTIS networks and investigating their performance in solving combinatorial optimization problems become a great necessity. In this paper, a parallel repetitive nearest neighbor algorithm for solving the symmetric traveling salesman problem on OTIS-Hypercube and OTIS-Mesh optoelectronic architectures is presented. This algorithm has been evaluated analytically and by simulation on both optoelectronic architectures in terms of number of communication steps, parallel run time, speedup, efficiency, cost and communication cost. The simulation results attained almost near-linear speedup and high efficiency among the two selected optoelectronic architectures, where OTIS-Hypercube gained better results in comparison with OTIS-Mesh.

Digital Image Scrambling Using 2D Cellular Automata

Abstract: A digital image scrambling method based on a 2D cellular automaton, specifically the well-known Game of Life, produces an effective image encryption technique.

A multiple-population genetic algorithm for branch coverage test data generation

Abstract: The software testing phase in the software development process is considered a time-consuming process. In order to reduce the overall development cost, automatic test data generation techniques based on genetic algorithms have been widely applied. This research explores a new approach for using genetic algorithms as test data generators to execute all the branches in a program. In the literature, existing approaches for test data generation using genetic algorithms are mainly focused on maintaining a single-population of candidate tests, where the computation of the fitness function for a particular target branch is based on the closeness of the input execution path to the control dependency condition of that branch. The new approach utilizes acyclic predicate paths of the program's control flow graph containing the target branch as goals of separate search processes using distinct island populations. The advantages of the suggested approach is its ability to explore a greater variety of execution paths, and in certain conditions, increasing the search effectiveness. When applied to a collection of programs with a moderate number of branches, it has been shown experimentally that the proposed multiple-population algorithm outperforms the single-population algorithm significantly in terms of the number of executions, execution time, time improvement, and search effectiveness.

The OTIS hyper hexa-cell optoelectronic architecture

Abstract: Optical transpose interconnection system (OTIS) is an optoelectronic architecture that promises to be a great choice for future-generation parallel systems. OTIS combines the advantages of electronic and optical links, where electronic links are used for short distances which require low material cost, and optical links are used for long distances which provide high speed network with low power consumption. Taking into account the advantageous characteristics of OTIS and based on the attractive properties of hyper hexa-cell (HHC) interconnection topology from low diameter and good minimum node degree, this paper introduces a new optoelectronic architecture referred to as OTIS hyper hexa-cell (OHHC). This paper also provides an evaluation and a comparison of the new topology with OTIS-mesh in terms of the following topological properties: size, diameter, maximum and minimum node degree, bisection width, total cost and optical cost. The results of this study proved the excellence of the proposed OHHC over OTIS-mesh in terms of diameter, minimum node degree, bisection width, and optical cost.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

[서평]「Computer Organization and Design, The Hardware/Software Interface」

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Political Optimizer: A novel socio-inspired meta-heuristic for global optimization

Abstract: This paper proposes a novel global optimization algorithm called Political Optimizer (PO), inspired by the multi-phased process of politics. PO is the mathematical mapping of all the major phases of politics such as constituency allocation, party switching, election campaign, inter-party election, and parliamentary affairs. The proposed algorithm assigns each solution a dual role by logically dividing the population into political parties and constituencies, which facilitates each candidate to update its position with respect to the party leader and the constituency winner. Moreover, a novel position updating strategy called recent past-based position updating strategy (RPPUS) is introduced, which is the mathematical modeling of the learning behaviors of the politicians from the previous election. The proposed algorithm is benchmarked with 50 unimodal, multimodal, and fixed dimensional functions against 15 state of the art algorithms. We show through experiments that PO has an excellent convergence speed with good exploration capability in early iterations. Root cause of such behavior of PO is incorporation of RPPUS and logical division of the population to assign dual role to each candidate solution. Using Wilcoxon rank-sum test, PO demonstrates statistically significant performance over the other algorithms. The results show that PO outperforms all other algorithms, and consistency in performance on such a comprehensive suite of benchmark functions proves the versatility of the algorithm. Furthermore, experiments demonstrate that PO is invariant to function shifting and performs consistently in very high dimensional search spaces. Finally, the applicability on real-world applications is demonstrated by efficiently solving four engineering optimization problems.