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Multi-objective genetic algorithm on hexagonal search for fast motion estimation

IET IMAGE PROCESSING 2019年第11期13卷 1892-1902页

作者： Palaniraj, Palanivel Sakthivel, Ganesan Annamalai Univ Dept Elect & Instrumentat Engn Annamalainagar 608002 Tamil Nadu India

Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10-15% search points over Diamond Search (DS) with negligible Peak Signal to Noise Ratio (PSNR) loss. Adapting an optimization will achieve a massive improvement on the search process. The proposed HS-GA method combines the HS with genetic algorithm (GA) to optimize ME. The HS-GA method reduces 72% on average search points pre block (ASPB) with PSNR loss of 2.5 dB and single generation. If the GA is executed with 10 generations the PSNR loss is reduced to 0.48 dB, but ASPB drastically increase to similar to 316%. The proposed work aims at multi objective on selection of better chromosomes and on control of generations to avoid the unnecessary generations to improve the GA. This process is named HSIGA(HS with Intelligent GA). The proposed HSIGA adapts five intelligent techniques like Histogram to decide new generations, Region based chromosome selection, Mutation on inner search gene, second rank valley point stopping criteria and interlaced HS to improve search process with negligible PSNR loss with in affordable search.

关键词： search problems genetic algorithms probability motion estimation cellular biophysics Poisson distribution negligible PSNR loss optimisation search process HS-GA method gene selection domain Poisson-distributed random genes ASPB region-based chromosome selection inner search gene multiobjective genetic algorithm hexagonal search fast motion estimation diamond search average search points pre-block second-rank valley point stopping criteria

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Optimization of stepwise varying width microchannel heat sink for high heat flux applications

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CASE STUDIES IN THERMAL ENGINEERING 2020年 18卷

作者： Abo-Zahhad, Essam M. Ookawara, Shinichi Radwan, Ali Elkady, M. F. El-Shazly, A. H. Egypt Japan Univ Sci & Technol Chem & Petrochem Engn Dept Alexandria Egypt Aswan Univ Fac Energy Engn Mech Power Engn Dept Aswan Egypt Tokyo Inst Technol Dept Chem Sci & Engn Tokyo 1528552 Japan Mansoura Univ Mech Power Engn Dept Mansoura 35516 Egypt City Sci Res & Technol Applicat Fabricat Technol Dept ATNMRI Alexandria Egypt Alexandria Univ Fac Engn Chem Engn Dept Alexandria 11432 Egypt

The semiconductor devices are widely employed for many applications. A significant amount of the heat must be dissipated properly to keep the devices' performance stable. Additionally, under high operation temperatures, a physical damage is possible due to thermal stresses that threaten the safety components and increase the failure rate. The development in the direction of more tightly packed electronic devices increases the challenges of offering an efficient cooling for these devices under high heat fluxes in a very restricted space. Hence, an advanced cooling technique is compulsory to attain the appropriate performance along with extending the lifespan of microelectronic devices. Stepwise varying width microchannel heat sink is considered one of the innovative cooling system from these microelectronic devices. In the current study, different designs of stepwise varying width microchannel heat sink were optimized, and their thermal performance was studied for more uniform cooling of microelectronics devices.

关键词： Stepwise varying width microchannel Heat sink multiobjective genetic algorithm Fin CPU

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Leakage reduction in water distribution system using efficient pressure management techniques. Case study: Nagpur, India

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WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2018年第6期18卷 2015-2027页

作者： Gupta, Aditya Dinesh Kulat, Kishore Visvesvaraya Natl Inst Technol Nagpur Maharashtra India

Leakages in water distribution system (WDS) are directly proportional to its operating pressure. Pressure management is becoming an important technique for reducing leakages in the water networks. This paper presents a pressure management technique for leakage reduction in north central WDS of Nagpur City, India, using variable speed pump and pressure reducing valves (PRVs). Variable speed pump is utilized for eliminating pressure deficiency during high demand and for reducing excess pressure causing leakage reduction during lower demand, by controlling the pump speed. PRVs have been used for further leakage reduction. This paper proposes a modified reference pressure algorithm for determining the location of valves in WDS. A multiobjective genetic algorithm (NSGA-II) is used to determine the optimized control value of pressure reducing valve with respect to change in demand pattern and to minimize the leakage rate in the WDS. Proposed pressure management technique leads to leakage reduction of 16.57% to 26.30% with respect to changes in demand pattern, causing daily average saving of 5.066 Ml. Minimum required pressure is maintained on every demand nodes to avoid pressure deficiency in WDS.

关键词： multiobjective genetic algorithm pressure management pressure reducing valves water distribution system (WDS)

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A multiscale approach to balance trade-offs among dam infrastructure, river restoration, and cost

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2018年第47期115卷 12069-12074页

作者： Roy, Samuel G. Uchida, Emi de Souza, Simone P. Blachly, Ben Fox, Emma Gardner, Kevin Gold, Arthur J. Jansujwicz, Jessica Klein, Sharon McGreavy, Bridie Mo, Weiwei Smith, Sean M. C. Vogler, Emily Wilson, Karen Zydlewski, Joseph Hart, David Univ Maine Senator George J Mitchell Ctr Sustainabil Solut Orono ME 04469 USA Univ Rhode Isl Dept Environm & Nat Resource Econ Kingston RI 02881 USA Univ New Hampshire Dept Civil & Environm Engn Durham NH 03824 USA Univ Maine Sch Econ Orono ME 04469 USA Univ Rhode Isl Dept Nat Resources Sci Kingston RI 02881 USA Univ Maine Dept Wildlife Fisheries & Conservat Biol Orono ME 04469 USA Univ Maine Dept Commun & Journalism Orono ME 04469 USA Univ Maine Sch Earth & Climate Sci Orono ME 04469 USA Rhode Isl Sch Design Dept Landscape Architecture Providence RI 02903 USA Univ Southern Maine Dept Environm Sci & Policy Gorham ME 04038 USA Univ Maine Maine Cooperat Fish & Wildlife Res Unit US Geol Survey Orono ME 04469 USA

Aging infrastructure and growing interests in river restoration have led to a substantial rise in dam removals in the United States. However, the decision to remove a dam involves many complex trade-offs. The benefits of dam removal for hazard reduction and ecological restoration are potentially offset by the loss of hydroelectricity production, water supply, and other important services. We use a multiobjective approach to examine a wide array of trade-offs and synergies involved with strategic dam removal at three spatial scales in New England. We find that increasing the scale of decision-making improves the efficiency of trade-offs among ecosystem services, river safety, and economic costs resulting from dam removal, but this may lead to heterogeneous and less equitable local-scale outcomes. Our model may help facilitate multilateral funding, policy, and stakeholder agreements by analyzing the trade-offs of coordinated dam decisions, including net benefit alternatives to dam removal, at scales that satisfy these agreements.

关键词： rivers dams multiobjective genetic algorithm trade-offs multicriteria decision analysis

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Development of nonuniform transmission line protection for accurate distance protection: Computational analysis of an adaptive distance relay characteristic

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2018年第4期28卷 e2514.1-e2514.17页

作者： Tavalaei, Jalal Habibuddin, Mohd Hafiz Naderipour, Amirreza Zin, Abdullah Asuhaimi Mohd Univ Teknol Malaysia Fac Elect Engn Johor Baharu 81310 Malaysia

' Numerical distance relays are used in modern power system to boost the performance and reduce the computational burden of relay. Quadrilateral zone of distance relay is more suitable and capable of boundary modification on protection scheme. In this paper, an adaptive distance relay is introduced to tackle the maloperation problems encountered in non-uniform structure of combined transmission system, in which the zone 1 of distance relay is modified. Two boundaries are modified with fifth degree of polynomial equation for quadrilateral zone characteristic. Appropriate objective functions are defined to minimize the increment in relay zone, maloperation rate, and boundary accuracy error. The multiobjective optimization problem is solved by using genetic algorithm. According to the extensive computational analysis results performed by MATLAB, the high accuracy of the proposed adaptive relay was achieved. Furthermore, the proposed method has the ability to encompass all faults and tackles the influence of various power system conditions by finding the optimized equation coefficients for the nonuniform structures.

关键词： combined transmission system multiobjective genetic algorithm quadrilateral zone characteristic reach accuracy

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Optimization of a stacked microchannel heat sink using nanofluids (AL₂O₃-H₂O) with multiobjective optimization of thermal resistance and pressure drop 5

Optimization of a stacked microchannel heat sink using nanof...

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5th IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)

作者： Zaidan, Husain Ahmad, Robiah Ghazali, Normah Mohd Middle Tech Univ Inst Technol Baghdad Iraq Univ Teknol Malaysia Razak Fac Technol & Informat Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Fac Mech Engn Johor Baharu 81310 Johor Malaysia

ISBN: (纸本)9781538662885

The single layer microchannel heat sink (MCHS) introduced a decade ago in microelectronics systems (MEMS) may no longer be an adequate solution. Possible extension of the layer of microchannels into a stacked system, by developing a multi-layer channel system, is investigated. The objectives of this study are to develop modified mathematical models for stacked (multi-layer) MCHS, using water and nanofluid of alumina-water based (Al2O3-H2O), at volumetric fraction phi of 1%, 2.5%, and 5%, for channel height of 287 mu m, finally, to optimize the thermal and hydrodynamic performance of the proposed model. The results of the optimization were carried out by multiobjective genetic algorithm, MOGA. The optimized thermal resistant and corresponding pressure drop, showed that at a channel height of 287 mu m and at volume fraction phi =5% water based alumina nanofluid provided the lowest value of the thermal resistance of 0.0282 /W for a multi-stacked (five layers) MCHS with pressure drop of 58.9 kPa as compared to 0.0867 C/W and 261.8 kPa for a single-layer MCHS at an optimum value for a (channel height to channel width) and beta (wall width to channel width) of 6.5 and 0.99 respectively.

关键词： Micro-channel heat sink MCHS thermal resistance optimization multiobjective genetic algorithm MOGA

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An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time

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JOURNAL OF INTELLIGENT MANUFACTURING 2011年第3期22卷 367-378页

作者： Zhang, Wenqiang Gen, Mitsuo Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

Mixed-model assembly lines (mALs) are becoming more and more important by producing different models of the same product on an assembly line. How to calculate the cycle time based on demand of different models also making problem more difficult. According to different work experiences and skill level, the processing time of a given task and the operating costs such as wages differ among workers. Appointing the proper worker to the proper station and assigning the suitable task to the suitable station in order to decrease the cycle time, increase the line efficiency, and reduce the total cost make the problem more complex. This paper proposes a new concept for calculating the cycle time based on demand ratio of each model and another one for calculating the human resource cost. A generalized Pareto-based scale-independent fitness function genetic algorithm (gp-siffGA) is described for solving mixed-model assembly lines balancing (mALB) problems to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. The gp-siffGA uses Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. Comparisons with existing multiobjective genetic algorithms demonstrate that our approach efficiently solves mALB problems.

关键词： Mixed-model assembly line balancing Worker allocation multiobjective genetic algorithm Demand ratio-based cycle time Pareto dominance relationship

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Optimisation design of medium frequency transformer for the offshore dc grid based on multi-objective genetic algorithm

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IET POWER ELECTRONICS 2017年第15期10卷 2157-2162页

作者： Zhang, Liang Zhang, Dan Shui, Henghua Yuan, Yubo Pei, Qian Zhu, Jihong Nanjing Inst Technol Sch Power Elect Engn 1 Hong Jing AveJiangning Sci Pk Nanjing Jiangsu Peoples R China Tsinghua Univ Dept Comp Sci & Technol East Main BldgRoom 9-310 Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Jiangsu Elect Power Co Res Inst Nanjing 211167 Jiangsu Peoples R China State Grid Suqian Power Supply Co Suqian 223800 Peoples R China

A DC/DC converter is a vital component for offshore DC grids, in which a medium frequency transformer (MFT) is the main element. Therefore, it is necessary to optimise the core material and the design method so that the loss of the MFT would be reduced. In this study, the multi-objective genetic algorithm is proposed, by which two MFTs with different core materials are both optimised. The magnetic flux density of the transformer core and the current density of the windings are introduced as the optimised variables. Also, the key equations for transformer design are put forward as the objective functions. Moreover, with constant iteration of the process of segmentation, parallel selection, mergence, recombination and variation, the Pareto optimal solution is determined. Furthermore, to verify the optimisation results, the temperature field and electric strength of the amorphous alloy MFT are calculated and simulated by finite-element analysis. Finally, a MFT prototype with an amorphous alloy core is built, and the experiment is carried out.

关键词： DC-DC power convertors offshore installations power transformers genetic algorithms cores distributed power generation finite-element analysis Pareto optimal solution windings magnetic flux density dc-dc converter multiobjective genetic algorithm offshore dc grid medium frequency transformer optimisation design

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multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables

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IET RENEWABLE POWER GENERATION 2010年第5期4卷 404-419页

作者： Katsigiannis, Y. A. Georgilakis, P. S. Karapidakis, E. S. Tech Univ Crete Dept Prod Engn & Management GR-73100 Khania Greece Natl Tech Univ Athens Sch Elect & Comp Engn GR-15780 Athens Greece Technol Educ Inst Crete Dept Environm & Nat Resources GR-73133 Khania Greece

The overall evaluation of small autonomous hybrid power systems (SAHPS) that contain renewable and conventional power sources depends on economic and environmental criteria, which are often conflicting objectives. The solution of this problem belongs to the field of non-linear combinatorial multiobjective optimisation. In a multiobjective optimisation problem, the target is not to find an optimal solution, but a set of non-dominated solutions called Pareto-set. The present article considers as an economic objective the minimisation of system's cost of energy (COE), whereas the environmental objective is the minimisation of the total greenhouse gas (GHG) emissions of the system during its lifetime. The main novelty of this article is that the calculation of GHG emissions is based on life cycle analysis (LCA) of each system's component. In LCA, the whole life cycle emissions of a component are taken into account, from raw materials extraction to final disposal/recycling. This article adopts the non-dominated sorting genetic algorithm (NSGA-II), which in combination with a proposed local search procedure effectively solves the multiobjective optimisation problem of SAHPS. Two main categories of SAHPS are examined with different energy storage: lead-acid batteries and hydrogen storage. The results indicate the superiority of batteries under both economic and environmental criteria.

关键词： Secondary cells secondary cells small autonomous hybrid power systems Pareto set optimisation power sources GHG emissions disposal-recycling Pollution detection and control Optimisation techniques nonlinear programming sorting total greenhouse gas emissions raw material extraction nondominated sorting genetic algorithm Combinatorial mathematics Other energy storage hydrogen storage multiobjective genetic algorithm Power system management, operation and economics optimum economic criteria air pollution control hybrid power systems nonlinear combinatorial multiobjective optimisation problem lead-acid batteries system cost of energy minimisation life cycle analysis environmental criteria power system economics genetic algorithms energy storage

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Hybrid Flow Shop with Unrelated Machines, Setup Time, and Work in Progress Buffers for Bi-Objective Optimization of Tortilla Manufacturing

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algorithmS 2018年第5期11卷 68-68页

作者： Yaurima-Basaldua, Victor Hugo Tchernykh, Andrei Villalobos-Rodriguez, Francisco Salomon-Torres, Ricardo Sonora State Univ Software Engn San Luis Rio Colorado 83455 Sonora Mexico CICESE Res Ctr Comp Sci Dept Ensenada 22860 Baja California Mexico South Ural State Univ Sch Elect Engn & Comp Sci Chelyabinsk 454080 Russia

We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting genetic algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

关键词： multiobjective genetic algorithm hybrid flow shop setup time energy optimization production environment

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