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multiobjective OPTIMIZATION OF PROCESS PLANT USING genetic algorithm

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2005年第4期5卷 425-437页

作者： Govindarajan, L. Karunanithi, T. Annamalai Univ Fac Engn & Technol Dept Chem Engn Annamalainagar 608002 India

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams-Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste byproducts. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

关键词： multiobjective genetic algorithm NSGA NSGA-II optimization process plant

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Combining multiobjective and Single-objective genetic algorithms in Heterogeneous Island Model

Combining Multiobjective and Single-objective Genetic Algori...

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2010 IEEE World Congress on Computational Intelligence

作者： Pilat, Martin Neruda, Roman Charles Univ Prague Fac Math & Phys Dept Theoret Comp Sci & Math Log Prague 1 Czech Republic Acad Sci Czech Republ Inst Comp Sci Prague 8 Czech Republic

ISBN: (纸本)9781424481262

The majority of multiobjective genetic algorithms is computationally expensive, therefore they often need to be parallelized before they can be used to solve practical tasks. Parallelization of multiobjective genetic algorithms is a relatively studied area, but no clearly winning approach has appeared yet. In this paper we present a novel parallel hybrid algorithm which combines multiobjective and single-objective genetic algorithms. We show that this algorithm can be successfully used to solve multiobjective optimization problems while outperforming more traditional parallel versions of multiobjective genetic algorithms.

关键词： genetic algorithms heterogeneous island model multiobjective genetic algorithm multiobjective optimization problem parallel hybrid algorithm single-objective genetic algorithm

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multiobjective genetic Fuzzy PID Control Design for Uncertain Dynamic Systems

Multiobjective Genetic Fuzzy PID Control Design for Uncertai...

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13th Biennial Ibero-American Conference on Artificial Intelligence (AI)

作者： Pires, Danubia Soares de Oliveira Serra, Ginalber Luiz Fed Inst Educ Sci & Technol Dept Electroelect Lab Computat Intelligence Appl Technol BR-65030005 Sao Luis Maranhao Brazil

ISBN: (纸本)9783642346538;9783642346545

In this paper, a robust fuzzy digital PID control strategy, via multiobjective genetic algorithm, based on the gain and phase margins specifications, with applications to uncertain dynamic systems with time delay, is proposed. A multiobjective genetic strategy is defined to tune the fuzzy controller parameters, so the gain and phase margins of the fuzzy control system are close to the specified ones. Computational results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the fuzzy PID control system compared to the specified ones and tracking of the reference trajectory compared to Ziegler-Nichols method.

关键词： robust control fuzzy system digital PID controller uncertain dynamic system multiobjective genetic algorithm

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multiobjective OPTIMIZATION OF PROCESS PLANT USING genetic algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2006年第3期6卷 315-327页

作者： Govindarajan, L. Karunanithi, T. Annamalai Univ Dept Chem Engn Fac Engn & Technol Annamalainagar 608002 Tamil Nadu India

The optimal design of large-scale process plant is difficult due to the presence of Pareto sets or nondominated solutions. Many conventional and advanced mathematical techniques had been adopted which have their own limitations in solving the complex design problem. In this paper, nondominant-sorted genetic algorithms NSGA and NSGA-II have been adopted for the optimal design of complex Williams-Otto model process plant. The plant consists of a reactor, separation system consisting of heat exchanger, decanter and distillation column. multiobjective optimization is used to maximize the profit, i.e. the return on investment, to maintain lesser use of costlier raw material and lesser disposal of the waste by products. So NSGA-II is employed in this study as an effective replacement for NSGA, classical genetic algorithm, conventional and traditional methods of optimization in solving multiobjective process design problems and to achieve fine-tuning of variables in determining Pareto optimal design parameters. NSGA-II method finding global optimal front has a significant effect on the design of control system for the real time and continuous robust control of complex process plant as each target vector provides proper direction and drives the process to multiobjective optimum conditions.

关键词： multiobjective genetic algorithm NSGA NSGA-II optimization process plant

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Design-space adaptation method for multiobjective and multidisciplinary optimization

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Chinese Journal of Aeronautics 2024年第8期37卷 166-189页

作者： Jongho JUNG Kwanjung YEE Shinkyu JEONG Department of Aerospace Engineering Seoul National UniversitySeoul 08826Republic of Korea Department of Mechanical Engineering Kyunghee UniversityYoungin 17104Republic of Korea

This paper developed a new method that adaptively adjusts a design space by considering the actual solution distribution of a problem to overcome the conventional design-space adaptation method that assumes the solutions distribution to be a normal distribution because the distributions of solutions are rarely normal distributions for real-world *** developed method was applied to nineteen multiobjective test functions that are widely used to evaluate the characteristics and performance of optimization *** results showed that this method adapted the design space to an appropriate design space where the solution existence probability was *** optimization performance achieved using the developed method was higher than that of the conventional ***,the developed method was applied to the conceptual design of an unmanned spacecraft to confirm its validity in real-world design and multidisciplinaryoptimization *** results showed that the Pareto solutions of the developed method were superior to those of conventional ***,the optimization efficiency with the developed method was improved by more than 1.4 times over that of the conventional *** this regard,the developed method has the potential to be applied to complicated real-world optimization problems to achieve better performance and efficiency.

关键词： multiobjective optimization multiobjective genetic algorithm Design-space adaptation Multidisciplinary optimization Hypersonic vehicle

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One instruction set computer with optimised polarity-tunable model of double gate CNTFETs

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IET CIRCUITS DEVICES & SYSTEMS 2020年第6期14卷 770-779页

作者： Ponnayan, Reena Monica Thazhathu, Sreedevi Vellithiruthi Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India

Emerging devices such as double gate carbon nanotube field effect transistors (DG CNTFETs) have opened up manifold possibilities for reconfigurable logic design. The thickness of gate oxide and the employment of inhomogeneous dielectrics over and under the carbon nanotubes (CNTs) impact the operation of DG CNTFETs. In this work, the dielectric constant and the thickness of the gate oxide are optimised to suppress the ambipolar conduction in CNTFETs. A multi-objective genetic algorithm-based approach is proposed to optimise these parameters. The contributions in this study are two-fold: firstly, a DG CNTFET is fabricated with the optimised parameters. By exploiting the ability to select the conduction behaviour using a second gate in the fabricated DG CNTFETs, the device is electrostatically programmed to behave as an n or p-type CNTFET. Secondly, a one instruction set computer is simulated with the optimised model of DG CNTFET. A universal static logic cell which implements 16 logic functions, a D-Latch and a D-FF were built with DG CNTFETs. The logic circuits with polarity-tunable DG CNTFETs outperform other logic structures. There is a 40% betterment in performance primarily due to the reduced number of logic levels and by 25% due to the reduced delay.

关键词： genetic algorithms logic circuits carbon nanotube field effect transistors permittivity semiconductor device models flip-flops one instruction set computer optimised polarity-tunable model double gate carbon nanotube field effect transistors gate oxide p-type CNTFET polarity-tunable DG CNTFET fabricated DG CNTFET double gate CNTFET reconfigurable logic design inhomogeneous dielectrics dielectric constant ambipolar conduction multiobjective genetic algorithm universal static logic cell logic circuits logic functions D-latch D-flip flops C

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Simultaneous optimization of layout and task schedule for robotic cellular manufacturing systems

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 102卷 396-407页

作者： Suemitsu, Issei Izui, Kazuhiro Yamada, Takayuki Nishiwaki, Shinji Noda, Akio Nagatani, Tatsuya Kyoto Univ Dept Mech Engn & Sci Sakyo Ku Kyoto 6068501 Japan Mitsubishi Electr Corp Tsukaguchi Honmachi 8-1-1 Amagasaki Hyogo Japan

Multi-robot cellular manufacturing systems utilize several articulated industrial robots that cooperatively perform a large number of complex operations when assembling products. To enhance the system performance when designing layouts for such robotic cellular manufacturing systems, the positions of robots and other manufacturing system components must be appropriately determined by considering the sequence of tasks the robots conduct during the assembly process. This paper proposes a new multiobjective layout design optimization technique for robotic cellular manufacturing system layouts that can simultaneously determine the positions of manufacturing components and also task scheduling. First, in this paper, sequence-pair representation is used to specify layout design candidates that inherently avoid interference between assembly system components, and the use of dummy components is introduced to represent layout areas that provide necessary and appropriate spacing between manufacturing system components. In addition, task allocations for each robot are considered as discrete design variables. The design criteria for robot cellular manufacturing system layout designs are clarified and the layout design problem is formulated as a multiobjective optimization problem. To solve the optimization problem, a method based on a multiobjective genetic algorithm is proposed and numerical examples are provided to demonstrate the effectiveness of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Robotic cellular manufacturing system Assembly layout Design optimization multiobjective genetic algorithm Task allocation Sequence-pair

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Optimized Virtual Sources Distributions for 3-D Ultrafast Diverging Wave Compounding Imaging: A Simulation Study

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2023年第10期70卷 1319-1328页

作者： Le Moign, Goulven Masson, Patrice Basset, Olivier Liebgott, Herve Quaegebeur, Nicolas Univ Sherbrooke Dept Mech Engn GAUS Sherbrooke PQ J1K 2R1 Canada Univ Lyon UJM St Etienne CREATIS INSA LyonCNRSUMR 5220UCBL1Inserm U1206 F-69100 Villeurbanne France

Ultrafast ultrasound imaging allows observing rapid phenomena;combined with 3-D imaging it has the potential to provide a more accurate analysis of organs which leads, in the end, to better diagnosis. Coherent compounding using diverging waves is commonly used to reconstruct high-quality images on large volumes while keeping the frame rate high enough to allow dynamic analysis. In practice, the virtual sources (VSs) that drive the diverging waves are often distributed in a deterministic way: following a regular grid, concentric rings, and spirals. Even though those deterministic distributions can offer various tradeoffs in terms of imaging performance, other distributions can be considered to improve imaging performance. It is herein suggested to look at alternative VSs distributions for optimizing the lateral resolution and the secondary lobes level (SLL) on several point spread functions (PSFs) by means of a multiobjective genetic algorithm. The optimization framework has led to seven pseudo-irregular distributions of VSs distributions that have not yet been found in the literature. An analysis of the imaging performance with a simulated phantom shows that these new distributions offer different tradeoffs between lateral resolution and contrast, respectively, measured on point-like reflectors and anechoic cysts. As an example, one of these optimized distributions improves the lateral resolution by 16% and gives equivalent contrast values on cysts and PSF isotropy properties, when compared to a concentric-rings-based distribution.

关键词： 2-D ultrasound probe 2-D ultrasound probe 3-D ultrafast ultrasound imaging 3-D ultrafast ultrasound imaging diverging wave compounding diverging wave compounding multiobjective genetic algorithm multiobjective genetic algorithm virtual sources (VSs) virtual sources (VSs)

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multiobjective evolutionary approach to cost-effective traffic grooming in unidirectional SONET/WDM rings

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PHOTONIC NETWORK COMMUNICATIONS 2009年第1期18卷 105-115页

作者： Biswas, Utpal Maulik, Ujjwal Mukhopadhyay, Anirban Naskar, Mrinal Kanti Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India

Traffic grooming in optical networks is the process of multiplexing and demultiplexing low-speed traffic streams onto high-speed wavelengths. The research in the domain of traffic grooming mainly focuses on minimizing number of SONET add/drop multiplexers (SADMs) in SONET/WDM rings and it has been shown that they can potentially be reduced by careful assignment of low-speed traffic streams onto high-speed wavelengths. However, the cost of the network not only depends on the number of SADMs, but also the number of wavelengths and the grooming ratio. It is often the case that all of them cannot be minimized simultaneously. In this article, the problem of minimization of cost of a SONET/WDM unidirectional ring has been modeled as a multiobjective optimization problem which simultaneously minimizes the number of SADMs, the number of wavelengths, and the grooming ratio. A popular multiobjective genetic algorithm (NSGA-II) has been used as the underlying optimization tool. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Performance of the proposed algorithm has been demonstrated on different network topologies.

关键词： Wavelength division multiplexing Traffic grooming SONET add/drop multiplexers Grooming ratio multiobjective genetic algorithm Pareto-optimality

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An Interactive Approach to multiobjective Clustering of Gene Expression Patterns

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第1期60卷 35-41页

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

Some recent studies have posed the problem of data clustering as a multiobjective optimization problem, where several cluster validity indices are simultaneously optimized to obtain tradeoff clustering solutions. A number of cluster validity index measures are available in the literature. However, none of the measures can perform equally well in all kinds of datasets. Depending on the dataset properties and its inherent clustering structure, different cluster validity measures perform differently. Therefore, it is important to find the best set of validity indices that should be optimized simultaneously to obtain good clustering results. In this paper, a novel interactive genetic algorithm-based multiobjective approach is proposed that simultaneously finds the clustering solution as well as evolves the set of validity measures that are to be optimized simultaneously. The proposed method interactively takes the input from the human decision maker (DM) during execution and adaptively learns from that input to obtain the final set of validity measures along with the final clustering result. The algorithm is applied for clustering real-life benchmark gene expression datasets and its performance is compared with that of several other existing clustering algorithms to demonstrate its effectiveness. The results indicate that the proposed method outperforms the other existing algorithms for all the datasets considered here.

关键词： Clustering interactive algorithm microarray gene expression multiobjective genetic algorithm Pareto optimality

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