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Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem  [PDF]
Abd Allah A. Mousa, Mohamed A. El-Shorbagy
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.330184
Abstract: This paper presents an enhanced Particle Swarm Optimization (PSO) algorithm applied to the reactive power compensation (RPC) problem. It is based on the combination of Genetic Algorithm (GA) and PSO. Our approach integrates the merits of both genetic algorithms (GAs) and particle swarm optimization (PSO) and it has two characteristic features. Firstly, the algorithm is initialized by a set of a random particle which traveling through the search space, during this travel an evolution of these particles is performed by a hybrid PSO with GA to get approximate no dominated solution. Secondly, to improve the solution quality, dynamic version of pattern search technique is implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective RPC.
TRUST REGION-PARTICLE SWARM FOR MULTI-OBJECTIVE ENGINEERING COMPONENT DESIGN PROBLEMS
Mohamed A. El-Shorbagy
Journal of Global Research in Mathematical Archives , 2013,
Abstract: In this paper, we apply a proposed approach for solving multi-objective engineering design problem (MOEDP) with multiple objectives. In the proposed approach, a reference point based multi-objective optimization (MOO) using a combination between trust region (TR) algorithm and particle swarm optimization (PSO). The integration of TR and PSO has improved the quality of the founded solutions, also it guarantees the faster converge to the Pareto optimal solution. TR has provided the initial set (close to the Pareto set as possible) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. Detailed numerical results on three different MOEDP are reported to demonstrate the effectiveness and advantages of the proposed algorithm for solving practical MOEDP. Keywords: Multi-objective engineering design problem; trust region; particle swarm optimization;
Reference Point Based TR-PSO for Multi-Objective Environmental/Economic Dispatch  [PDF]
Ahmed Ahmed El-Sawy, Zeinab Mohamed Hendawy, Mohamed A. El-Shorbagy
Applied Mathematics (AM) , 2013, DOI: 10.4236/am.2013.45110
Abstract:

A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.

Separation Method in the Problem of a Beam-Plasma Interaction in a Cylindrical Warm Plasma Waveguide  [PDF]
Khaled H. El-Shorbagy, Abdelrahman S. Al-Fhaid, Mohamed A. Al-Ghamdi
Journal of Modern Physics (JMP) , 2011, DOI: 10.4236/jmp.2011.210136
Abstract: The stabilization effect of a strong HF electric field on beam-plasma instability in a cylindrical warm plasma waveguide is discussed. A mathematical technique “separation method” applied to the two-fluid plasma model to separate the equations, which describe the system, into two parts, temporal and space parts. Plasma electrons are considered to have a thermal velocity. It is shown that a HF electric field has no essential influence on dispersion characteristics of unstable surface waves excited in a warm plasma waveguide by a low-density electron beam. The region of instability only slightly narrowing and the growth rate decreases by a small parameter and this result has been reduced compared to cold plasma. Also, it is found that the plasma electrons have not affected the solution of the space part of the problem.
Hybrid Genetic Algorithm with K-Means for Clustering Problems  [PDF]
Ahamed Al Malki, Mohamed M. Rizk, M. A. El-Shorbagy, A. A. Mousa
Open Journal of Optimization (OJOp) , 2016, DOI: 10.4236/ojop.2016.52009
Abstract: The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.
Binary-Real Coded Genetic Algorithm Based k-Means Clustering for Unit Commitment Problem  [PDF]
Mai A. Farag, M. A. El-Shorbagy, I. M. El-Desoky, A. A. El-Sawy, A. A. Mousa
Applied Mathematics (AM) , 2015, DOI: 10.4236/am.2015.611165
Abstract: This paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit; while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results.
High-sensitivity C-reactive protein as a marker of cardiovascular risk in obese children and adolescents  [PDF]
Hatem Hamed El-shorbagy, Ibraheim Abdel-aziz Ghoname
Health (Health) , 2010, DOI: 10.4236/health.2010.29158
Abstract: Background and aim of the work: High-sensiti- vity C-reactive protein (hsCRP) is a marker of low grade inflammatory state, which characterises an atherosclerotic process. The metabolic syndrome is associated with insulin resistance and a systemic low-grade inflammatory state. These disorders may arise at a very early age in obese children. We aimed to assess the utility of (hsCRP) as a marker of cardiovascular risk in obese children and adolescents. Patients and methods: This study was conducted on 100 obese child and adolescents (6-16 years). 50 apparently healthy children of matched age and sex served as control. All patients and controls were subjected to: 1-complete history taking. 2-anthropometric measurements and clinical examination including body height, weight, waist circumference, body mass index and blood pressure. 3-laboratory investigations in- cluding fasting glucose, lipid profile, apolipoprotiens and (hsCRP) were assessed. Metabolic syndrome patients had to meet three out of five criteria: concentration of triglycerides (TG) ≥ 110 mg/dL, high density lipoprotein cholesterol (HDL- C) ≤ 40 mg/dL, waist circumference ≥ 90th percentile, glucose concentration ≥ 110 mg/dL, and systolic or diastolic blood pressure ≥ 90th percentile. Results, height, weight BMI and blood pressure were significantly higher in the obese than the control. Obese group had significantly higher (hsCRP) levels than control group, (p < 0.01) and significantly higher LDL-C, triglyceride (TG), and lower HDL-C than the control group. Log (hsCRP) showed a positive correlation with BMI (p < 0.001), blood pressure, and TG. The pre- valence of the metabolic syndrome was 24%. Mean concentrations of (hsCRP) were higher among patients who had the metabolic syndrome. Among whom, 35% had a concentration of (hsCRP) > 3.0 mg/L, a concentration considered to place adults at high risk for cardiovascular disease. In multiple logistic regression analysis only abdominal obesity was significantly associated with (hsCRP). Conclusion: me- tabolic syndrome and abdominal obesity among our patients predispose to cardiovascular disease later in life through early low grade inflammation. (hsCRP) is one of the inflammatory markers that can be easily estimated in these patients.
Comparison of the Predictive Value of Antral Follicle Count, Anti-Müllerian Hormone and Follicle-Stimulating Hormone in Women Following GnRH-Antagonist Protocol for Intracytoplasmic Sperm Injection  [PDF]
Shahinaz H. El-Shorbagy
Open Journal of Obstetrics and Gynecology (OJOG) , 2017, DOI: 10.4236/ojog.2017.74045
Abstract: Background: Prediction of ovarian response is one of the prerequisites for women undergoing intracytoplasmic sperm injection (ICSI) treatment prior to the first controlled ovarian stimulation (COS) cycle. Predictive factors may be variable in patients pre-treated with oral contraceptives (OC) for scheduling purposes. Objective: To evaluate antral follicle count (AFC), anti-müllerian hormone (AMH) and basal follicle stimulating hormone (FSH) for predicting ovarian responses in patients under controlled ovarian hyperstimulation randomized to receive either oral contraceptives (OC) or no treatment (non-OC) prior to their first controlled ovarian stimulation (COS) cycle. Study Design: One hundred infertile women randomized to receive OC treatment or no treatment, prior to their first COS cycle; were stimulated with Gonadotropin Releasing Hormone (GnRH) antagonist protocol. During the early follicular phase (day 2) of the two subsequent cycles (cycle A & cycle B) sonographic (AFC, ovarian volume) and endocrine data (AMH, basal FSH) were recorded. Transvaginal ultrasound was performed for all patients to monitor the ovarian response. Total number of oocytes retrieved and number of generated embryos were recorded and patients were categorized according to retrieved oocytes as poor (oocytes < 5), normal (oocytes 5 - 12) or high responders (oocytes > 12). Result(s): AFC, AMH and basal FSH were lower in users than in non-users of hormonal contraception. Poor responders showed less number of oocytes retrieved and had lower AFC and AMH but a higher basal FSH level was recorded in both groups (OC and non-OC). Conclusion: The better predictive value of AMH or AFC, as a single test or in combination will prevent cycle cancellations due to too low or too high ovarian response. AMH in OC group is not affected by OC pretreatment and is superior to other parameters, while AFC is superior to AMH and basal FSH in non-OC group.
TRUST REGION-PARTICLE SWARM FOR MULTI-OBJECTIVE ENGINEERING COMPONENT DESIGN PROBLEMS
Mohammed Abd El-Rahman El-Shorbagy,Ahmed El-Sawy,Zeinab Hendawy
Journal of Global Research in Mathematical Archives , 2013,
Abstract: In this paper, we apply a proposed approach for solving multi-objective engineering design problem (MOEDP) with multiple objectives. In the proposed approach, a reference point based multi-objective optimization (MOO) using a combination between trust region (TR) algorithm and particle swarm optimization (PSO). The integration of TR and PSO has improved the quality of the founded solutions, also it guarantee the faster converge to the Pareto optimal solution. TR has provided the initial set (close to the Pareto set as possible) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. Detailed numerical results on three different MOEDP are reported to demonstrate the effectiveness and advantages of the proposed algorithm for solving practical MOEDP.
REB-instability with magnetized inhomogeneous warm plasma
Kh. H. El-Shorbagy,R. N. El-Sharif,B. M. Dakhel
Advanced Studies in Theoretical Physics , 2012,
Abstract:
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