Yazar "Abu-Mahfouz, Adnan M." seçeneğine göre listele

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    Program Source-Code Re-Modularization Using a Discretized and Modified Sand Cat Swarm Optimization Algorithm
    (Mdpi, 2023) Arasteh, Bahman; Seyyedabbasi, Amir; Rasheed, Jawad; Abu-Mahfouz, Adnan M.
    One of expensive stages of the software lifecycle is its maintenance. Software maintenance will be much simpler if its structural models are available. Software module clustering is thought to be a practical reverse engineering method for building software structural models from source code. The most crucial goals in software module clustering are to minimize connections between created clusters, maximize internal connections within clusters, and maximize clustering quality. It is thought that finding the best software clustering model is an NP-complete task. The key shortcomings of the earlier techniques are their low success rates, low stability, and insufficient modularization quality. In this paper, for effective clustering of software source code, a discretized sand cat swarm optimization (SCSO) algorithm has been proposed. The proposed method takes the dependency graph of the source code and generates the best clusters for it. Ten standard and real-world benchmarks were used to assess the performance of the suggested approach. The outcomes show that the quality of clustering is improved when a discretized SCSO algorithm was used to address the software module clustering issue. The suggested method beats the previous heuristic approaches in terms of modularization quality, convergence speed, and success rate.
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    Sand cat swarm optimization-based feedback controller design for nonlinear systems
    (Cell Press, 2023) Aghaei, Vahid Tavakol; SeyyedAbbasi, Amir; Rasheed, Jawad; Abu-Mahfouz, Adnan M.
    The control of the open loop unstable systems with nonlinear structure is challenging work. In this paper, for the first time, we present a sand cat swarm optimization (SCSO) algorithm-based state feedback controller design for open-loop unstable systems. The SCSO algorithm is a newly proposed metaheuristic algorithm with an easy-to-implement structure that can efficiently find the optimal solution for optimization problems. The proposed SCSO-based state feedback controller can successfully optimize the control parameters with efficient convergence curve speed. In order to show the performance of the proposed method, three different nonlinear control systems such as an Inverted pendulum, a Furuta pendulum, and an Acrobat robot arm are considered. The control and optimization performances of the proposed SCSO algorithm are compared with well-known metaheuristic algorithms. The simulation results show that the proposed control method can either outperform the compared metaheuristic-based algorithms or have competitive results.