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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2020年第4期35卷 1997-2012页

作者： Oruc, Ridvan Baklacioglu, Tolga Turan, Onder Kastamonu Univ Sch Civil Aviat TR-37150 Kastamonu Turkey Eskisehir Tech Univ Fac Aeronaut & Astronaut TR-26555 Eskisehir Turkey

In this study, design analysis of mixed flow conceptual turbofan engine was accomplished by the use of MATLAB program. Thrust specific fuel consumption (SFC), specific thrust (ST), and overall efficiency (n(o)) objective functions, which are the parameters indicating engine performance, were optimized both "with" and "without" afterburner cases throughout the range where bypass ratio and fan pressure ratio vary between 0.4 <= alpha <= 1.3 and 1.2 <= pi(c)' <= 2.1, respectively. A conceptual turbofan engine was utilized for the analysis phase. particle swarm optimization (PSO) and multi objective particle swarm optimization (MOPSO) modeling approaches were used so as to achieve the optimum solution. Since there is no study with MOPSO in the literature on this subject, this study is the first study using MOPSO method in parametric analysis of turbofan engine. The optimal values of SFC, ST ve n(o) for the "with afterburner" condition were found 52. 4025, 1013.3 and %21.13 respectively, with alpha and pi(c)' being 0.4 and 1.2 respectively. The optimal values of SFC, ST ve n(o) for the "without afterburner" condition were found 20.8887, 658.3483 and %30.1234 respectively. alpha ve pi(c)' pairs, which providing the optimum values for SFC, ST and n(o) for the "without afterburner" condition, were found to be (1.3 and 1.2), (0.4 and 1.2) and (1.3 and 1.2) respectively.

关键词： Turbofan engine particle swarm optimization specific thrust thrust specific fuel consumption multi objective particle swarm optimization

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Identification of Biomarkers for Arsenicosis Employing multiple Kernel Learning Embedded multiobjective swarm Intelligence

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2023年第2期22卷 383-392页

作者： Dey, Anirban Das Sharma, Kaushik Sanyal, Tamalika Bhattacharjee, Pritha Bhattacharjee, Pritha Univ Calcutta Dept Appl Phys Kolkata 700009 India Univ Calcutta Dept Environm Sci Kolkata 700019 India

Arsenic is a carcinogen, and long-term exposure to it may result in the development of multi-organ disease. Understanding the underlying intricate molecular network of toxicity and carcinogenicity is crucial for identifying a small set of differentially expressed biomarker genes to predict the risk of the exposed population. In this paper, a multiple kernel learning (MKL) embedded multi-objective swarm intelligence technique has been proposed to identify the candidate biomarker genes from the transcriptomic profile of arsenicosis samples. To achieve the optimal classification accuracy along with the minimum number of genes, a multi-objective random spatial local best particle swarm optimization (MO-RSplbestPSO) has been utilized. The proposed MO-RSplbestPSO also guides the multiple kernel learning mechanism which provides data specific classification. The proposed computational framework has been applied to the developed whole genome DNA microarray prepared using blood samples collected from a specific arsenic exposed area of the Indian state of West Bengal. A set of twelve biomarker genes, with four novel genes, are successfully identified for the classification of exposure to arsenic and its subcategories, which can be used as future prognostic biomarkers for screening of arsenic exposed populations. Also, the biological significance of each gene is detailed to delineate the complex molecular networking and mode of toxicity.

关键词： Biomarkers Arsenic particle swarm optimization Biology optimization Kernel Sociology multi objective particle swarm optimization gene selection microarray data biomarker arsenicosis

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PID based particle swarm optimization in Offices Light Control

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IFAC-PapersOnLine 2018年第4期51卷 382-387页

作者： Copot, Cosmin Thi, Thoa Mac Ionescu, Clara University of Antwerp Department of Electromechanics Op3Mech Groenenborgerlaan 171 Antwerp2020 Belgium Technologiepark 914 Ghent9052 Belgium School of Mechanical Engineering Hanoi University of Science and Technology No.1 Dai Co Viet Viet Nam

In this paper, a particle swarm optimization called multi-objective particle swarm optimization (MOPSO) with an accelerated update methodology is employed to tune Proportional Integral Derivative (PID) controller for a light control system. Nowadays, the demand of energy has exponentially increased and therefore is necessary to use the electric energy efficiently. Here, we tackle the use of light in offices where it is preferable to provide uniform illumination over the entire workplace by combining both natural and artificial lighting. The focus of this research is to regulate the light amount in a room at a constant level, irrespective of the disturbances from outside such as weather conditions. Thus, a control system for closed-loop regulation of the light amount in building rooms is designed. The main benefits would be a higher level of comfort and a continuous saving of energy. The obtained results verify that the MOPSO is able to perform appropriately in complex systems such as light control environment. © 2018

关键词： Proportional control systems Energy efficiency multiobjective optimization particle swarm optimization (PSO) Three term control systems Two term control systems Artificial lighting Constant level Electric energies Light control Light control systems multi objective particle swarm optimization Proportional integral derivative controllers Uniform illumination

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Efficient multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications

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INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2018年第3期14卷 18-37页

作者： Paramanandham, Nirmala Rajendiran, Kishore SSN Coll Engn Madras Tamil Nadu India

This article describes how image fusion has taken giant leaps and emerged as a promising field with diverse applications. A fused image provides more information than any of the source images and it is very helpful in surveillance applications. In this article, an efficient multi focus image fusion technique is proposed in cascaded wavelet transform domain using swarm intelligence and spatial frequency (SF). Spatial frequency is used for computing the activity level and consistency verification (CV) based decision map is employed for acquiring the final fused coefficients. Justification for employing SF and CV is also discussed. This technique performs well compared to existing techniques even when the source images are severely blurred. The proposed framework is evaluated using quantitative metrics, such as root mean square error, peak signal to noise ratio, mean absolute error, percentage fit error, structural similarity index, standard deviation, mean gradient, Petrovic metric, SF, feature mutual information and entropy. Experimental outcomes demonstrate that the proposed technique outperforms the state-of-the art techniques, in terms of visual impact as well as objective assessment.

关键词： Consistency Verification Discrete Wavelet Transform Image Fusion multi objective particle swarm optimization Spatial Frequency Stationary Wavelet Packet Transform

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Test Case Prioritization using multi objective particle swarm Optimizer

Test Case Prioritization using Multi Objective Particle Swar...

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International Conference on Signal Propagation and Computer Technology (ICSPCT)

作者： Tyagi, Manika Malhotra, Sona Kurukshetra Univ UIET Dept CSE Kurukshetra 132119 Haryana India

ISBN: (纸本)9781479931408

The goal of regression testing is to validate the modified software. Due to the resource and time constraints, it becomes necessary to develop techniques to minimize existing test suites by eliminating redundant test cases and prioritizing them. This paper proposes a 3-phase approach to solve test case prioritization. In the first phase, we are removing redundant test cases by simple matrix operation. In the second phase, test cases are selected from the test suite such that selected test cases represent the minimal set which covers all faults and also at the minimum execution time. For this phase, we are using multi objective particle swarm optimization (MOPSO) which optimizes fault coverage and execution time. In the third phase, we allocate priority to test cases obtained from the second phase. Priority is obtained by calculating the ratio of fault coverage to the execution time of test cases, higher the value of the ratio higher will be the priority and the test cases which are not selected in phase 2 are added to the test suite in sequential order. We have also performed experimental analysis based on maximum fault coverage and minimum execution time. The proposed MOPSO approach is compared with other prioritization techniques such as No Ordering, Reverse Ordering and Random Ordering by calculating Average Percentage of fault detected (APFD) for each technique and it can be concluded that the proposed approach outperformed all techniques mentioned above.

关键词： Regression Testing Test case selection Test case prioritization multi objective particle swarm optimization

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multi objective Simulation-optimization Approach in Pollution Spill Response Management Model in Reservoirs

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WATER RESOURCES MANAGEMENT 2013年第6期27卷 1851-1865页

作者： Saadatpour, Motahareh Afshar, Abbas Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Mahab Ghodss Consulting Engn Affairs Integrated S Tehran Iran Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Envirohydroinformat Res Tehran Iran

In this study, a Pollution Spill Response Management Model (PSRMM) is developed to provide an emergency response on reservoir operation during accidental injection of hazardous material to reservoirs. PSRMM consist of spatial system analyzing (SSA) model, 2D hydrodynamic and water quality simulation model (CE-QUAL-W2), and multi-objective particle swarm optimization (MOPSO) algorithm. CE-QUAL-W2 model is applied for spatial and temporal analysis of water body in simulation routine of PSRMM. Also, in an advanced modeling framework, CE-QUAL-W2 is coupled with MOPSO algorithm to obtain desirable near optimal reservoir operation strategy and/or emergency planning in selective withdrawal framework. The simulation-optimization (SO) routine of PSRMM provides pareto optimum reservoir operation strategy in selective scheme to minimize reservoir cleanup time and to reduce the magnitude and frequency of water quality standard violations. The proposed tool is applied in Ilam reservoir in Iran, as a multipurpose hydraulic project providing water for drinking, irrigation, and flood control during an accident spill of conservative hazardous material. Different scenarios are defined and tested employing the proposed PSRMM for managing accidental spill of conservative pollutant into the reservoir.

关键词： Accident pollution spill CE-QUAL-W2 multi objective particle swarm optimization Simulation-optimization

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Shunt Capacitor Operation under Large-Scale Photovoltaic Energy Penetration

Shunt Capacitor Operation under Large-Scale Photovoltaic Ene...

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General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Suzuki, Ken Suzuki, Toshiko Iwamoto, Shinichi Elect Power Dev Co Tokyo Japan Waseda Univ Dept Elect Engn & Biosci Tokyo Japan

ISBN: (纸本)9781479913039

Japan plans to introduce a large amount of photovoltaic (PV) power generation capacity in order to help combat climate change. In this paper, we focus on reactive power in large-scale PV systems. First, we propose a two-layered shunt capacitor (SC) scheduling method using multi objective particle swarm optimization (MOPSO) a meta-heuristic technique and dynamic programming (DP). By using MOPSO, cost efficiency and reliability in power system planning and operation can be more accurately analyzed, and by using DP, switching times can be assessed. We then propose an effective static var compensator (SVC) control method for use in situations where PV outputs fluctuate sharply over short time intervals in order to control reactive power flows caused by changing PV output. Based on simulations run on a modified Ward-Hale 6-bus system to verify the validity of the method, we can confirm that its capability over a normal voltage range for controlling power systems with a large degree of penetration.

关键词： Shunt Capacitor Reactive Power Voltage Stability Photovoltaic Power Dynamic Programming multi objective particle swarm optimization

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Shunt Capacitor Operation under Large-Scale Photovoltaic Energy Penetration

Shunt Capacitor Operation under Large-Scale Photovoltaic Ene...

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IEEE Power and Energy Society General Meeting

作者： Ken Suzuki Toshiko Suzuki Shinichi Iwamoto Department of Electrical Engineering and Bioscience Waseda University Tokyo Japan.

ISBN: (纸本)9781479913022

Japan plans to introduce a large amount of photovoltaic (PV) power generation capacity in order to help combat climate change. In this paper, we focus on reactive power in large-scale PV systems. First, we propose a two-layered shunt capacitor (SC) scheduling method using multi objective particle swarm optimization (MOPSO)—a meta-heuristic technique— and dynamic programming (DP). By using MOPSO, cost efficiency and reliability in power system planning and operation can be more accurately analyzed, and by using DP, switching times can be assessed. We then propose an effective static var compensator (SVC) control method for use in situations where PV outputs fluctuate sharply over short time intervals in order to control reactive power flows caused by changing PV output. Based on simulations run on a modified Ward-Hale 6-bus system to verify the validity of the method, we can confirm that its capability over a normal voltage range for controlling power systems with a large degree of penetration.

关键词： Shunt Capacitor Reactive Power Voltage Stability Photovoltaic Power Dynamic Programming multi objective particle swarm optimization Reactive power Voltage stability shunt capacitor dynamic programming Time intervals Power flow Power system planning Polydnaviridae data processing Photovoltaic energy

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Modified multi-objective particle swarm optimization for electromagnetic absorber design

Modified multi-objective particle swarm optimization for ele...

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2007 Asia-Pacific Conference on Applied Electromagnetics

作者： Chamaani, Somayyeh Mirtaheri, Seyed Abdullah Teshnehlab, Mohammad Shooredeli, Mahdi Aliyari KN Toosi Univ Technol Fac Elect Engn Tehran Iran

ISBN: (纸本)9781424414345

Use of multi-objective particle swarm optimization for designing of planar multilayered electromagnetic absorbers and finding optimal Pareto front is described. The achieved Pareto presents optimal possible trade offs between thickness and reflection coefficient of absorbers. particle swarm optimization method in comparison with most of optimization algorithms such as genetic algorithms is simple and fast. But the basic form of multi-objective particle swarm optimization may not obtain the best Pareto. We applied some modifications to make it more efficient in finding optimal Pareto front. Comparison with reported results in previous articles confirms the ability of this algorithm in finding better solutions.

关键词： multilayered absorber Pareto optimization multi objective particle swarm optimization bandwidth thickness

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Modified multi-objective particle swarm optimization for electromagnetic absorber design

Modified Multi-objective Particle Swarm Optimization for ele...

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Asia-Pacific Conference on Applied Electromagnetics, APACE

作者： Somayyeh Chamaani Seyed Abdullah Mirtaheri Mohammad Teshnehlab Mahdi Aliyari Shooredeli K.N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781424414345;1424414350

关键词： multilayered absorber Pareto optimization multi objective particle swarm optimization Bandwidth Thickness particle swarm optimization Pareto optimisation Bandwidth ELECTROMAGNETIC absorbers optimization algorithms Reflection coefficient FINDINGS

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