Artykuły w czasopismach na temat „Optimization”
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Mukherjee, Manasij, i John Regehr. "Hydra: Generalizing Peephole Optimizations with Program Synthesis". Proceedings of the ACM on Programming Languages 8, OOPSLA1 (29.04.2024): 725–53. http://dx.doi.org/10.1145/3649837.
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Optimizing compilers rely on peephole optimizations to simplify combinations of instructions and remove redundant instructions. Typically, a new peephole optimization is added when a compiler developer notices an optimization opportunity---a collection of dependent instructions that can be improved---and manually derives a more general rewrite rule that optimizes not only the original code, but also other, similar collections of instructions. In this paper, we present Hydra, a tool that automates the process of generalizing peephole optimizations using a collection of techniques centered on program synthesis. One of the most important problems we have solved is finding a version of each optimization that is independent of the bitwidths of the optimization's inputs (when this version exists). We show that Hydra can generalize 75% of the ungeneralized missed peephole optimizations that LLVM developers have posted to the LLVM project's issue tracker. All of Hydra's generalized peephole optimizations have been formally verified, and furthermore we can automatically turn them into C++ code that is suitable for inclusion in an LLVM pass.
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Sartoros, Christine, Douglas M. Goltz i Eric D. Salin. "Program Considerations for Simplex Optimization of Ion Lenses in ICP-MS". Applied Spectroscopy 52, nr 5 (maj 1998): 643–48. http://dx.doi.org/10.1366/0003702981944292.
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The performance of an inductively coupled plasma mass spectrometer (ICP-MS) is dependent on the ion optic bias potentials. A discussion of the multielement optimization of the ICP-MS ion optics bias potentials using a Simplex algorithm is presented. Three objective functions were tested: a function developed by Leary; the combined ratio method (CRM); and the Euclidean distance from multicriteria target vector optimization. Both the Leary and the target vector optimization's performances were comparable, whereas the CRM optimizations placed an emphasis on obtaining similar signal intensities. Experiments determined that an initial Simplex starting size of 20% of the parameter space was optimal. A method for the selection of an appropriate target vector by predicting analyte signal intensity was also investigated. Signal intensities for all elements could be predicted with an acceptable margin of error (10–30%), provided that the same conditions were used. Comparisons of optimizations using a single mid-mass element vs. multielement optimizations revealed that the multielement approach is only slightly better. If the analyst wished to optimize lens settings to favor heavy or light elements, then an average mass was better than a mid-mass optimization.
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Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization". Applied Mechanics and Materials 201-202 (październik 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.
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A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality.
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Nan, Yang. "An Improved Ant Colony Optimization Algorithm Based on Immunization Strategy". Advanced Materials Research 490-495 (marzec 2012): 66–70. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.66.
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Ant colony optimization has been become a very useful method for combination optimization problems. Based on close connections between combination optimization and continuous optimization, nowadays some scholars have studied to apply ant colony optimization to continuous optimization problems, and proposed some continuous ant colony optimizations. To improve the performance of those continuous ant colony optimizations, here the principles of evolutionary algorithm and artificial immune algorithm have been combined with the typical continuous Ant Colony Optimization, and the adaptive Cauchi mutation and thickness selection are used to operate the ant individual, so a new Immunized Ant Colony Optimization is proposed.
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Rajeanderan Revichandran, Jaffar Syed Mohamed Ali, Moumen Idres i A. K. M. Mohiuddin. "A Review of HVAC System Optimization and Its Effects on Saving Total Energy Utilization of a Building". Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 93, nr 1 (25.03.2022): 64–82. http://dx.doi.org/10.37934/arfmts.93.1.6482.
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The paper illustrates the review on the optimizations studies of HVAC systems based on three main methods – HVAC operational variables optimization, optimization of control parameters in HVAC system and parameter optimization in building models. For the HVAC system’s operational variables, the optimization process is based on the common and prescient energy utilization models. Thus, by comparing both, the non-common HVAC system models can get better output of energy reduction. Based on most of the studies, the occupancies thermal comfort requirements, are represented by the indoor air quality (IAQ) or the predicted mean vote (PMV) indexes. Comparing both requirements, the PMV index had a better overall energy reduction output of 47% and estimated annual energy reduction of 2,769 kg/year. Meanwhile, in optimization of HVAC’s control parameters, its overall aim is to achieve a better response output of the HVAC system in order to prevent energy wastage. Among this different optimization’s controller, the fuzzy logic tuning optimization has a better overall energy reduction. On the other hand, the parameter optimization in building model approach is performed before the construction of the structure itself, where multiple construction parameters are considerations in the design. In overall, when different tools for building parameter and model optimization are compared, the EXRETopt by using PMV comfort index approximately reduces 62% of the energy utilization.
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Seth, Ashish. "Regression Test Case Optimization Using Ant Colony Optimization". Journal of Advanced Research in Dynamical and Control Systems 12, nr 3 (20.03.2020): 111–18. http://dx.doi.org/10.5373/jardcs/v12i3/20201172.
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Batay, Sagidolla, Bagdaulet Kamalov, Dinmukhamed Zhangaskanov, Yong Zhao, Dongming Wei, Tongming Zhou i Xiaohui Su. "Adjoint-Based High-Fidelity Concurrent Aerodynamic Design Optimization of Wind Turbine". Fluids 8, nr 3 (28.02.2023): 85. http://dx.doi.org/10.3390/fluids8030085.
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To evaluate novel turbine designs, the wind energy sector extensively depends on computational fluid dynamics (CFD). To use CFD in the design optimization process, where lower-fidelity approaches such as blade element momentum (BEM) are more popular, new tools to increase the accuracy must be developed as the latest wind turbines are larger and the aerodynamics and structural dynamics become more complex. In the present study, a new concurrent aerodynamic shape optimization approach towards multidisciplinary design optimization (MDO) that uses a Reynolds-averaged Navier–Stokes solver in conjunction with a numerical optimization methodology is introduced. A multidisciplinary design optimization tool called DAFoam is used for the NREL phase VI turbine as a baseline geometry. Aerodynamic design optimizations in terms of five different schemes, namely, cross-sectional shape, pitch angle, twist, chord length, and dihedral optimization are conducted. Pointwise, a commercial mesh generator is used to create the numerical meshes. As the adjoint approach is strongly reliant on the mesh quality, up to 17.8 million mesh cells were employed during the mesh convergence and result validation processes, whereas 2.65 million mesh cells were used throughout the design optimization due to the computational cost. The Sparse Nonlinear OPTimizer (SNOPT) is used for the optimization process in the adjoint solver. The torque in the tangential direction is the optimization’s merit function and excellent results are achieved, which shows the promising prospect of applying this approach for transient MDO. This work represents the first attempt to implement DAFoam for wind turbine aerodynamic design optimization.
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Masarik, Jozef. "A thermodynamically motivated optimization algorithm: Circular wheel balance optimization". Applications of Mathematics 30, nr 6 (1985): 413–24. http://dx.doi.org/10.21136/am.1985.104171.
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Xu, Liansong, i Dazhi Pan. "Multi-objective Optimization Based on Chaotic Particle Swarm Optimization". International Journal of Machine Learning and Computing 8, nr 3 (czerwiec 2018): 229–35. http://dx.doi.org/10.18178/ijmlc.2018.8.3.692.
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Celik, Yuksel, i Erkan Ulker. "An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization". Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/370172.
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Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
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Satoh, Shigehisa, Kazuhiro Kusano i Mitsuhisa Sato. "Compiler Optimization Techniques for OpenMP Programs". Scientific Programming 9, nr 2-3 (2001): 131–42. http://dx.doi.org/10.1155/2001/189054.
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We have developed compiler optimization techniques for explicit parallel programs using the OpenMP API. To enable optimization across threads, we designed dataflow analysis techniques in which interactions between threads are effectively modeled. Structured description of parallelism and relaxed memory consistency in OpenMP make the analyses effective and efficient. We developed algorithms for reaching definitions analysis, memory synchronization analysis, and cross-loop data dependence analysis for parallel loops. Our primary target is compiler-directed software distributed shared memory systems in which aggressive compiler optimizations for software-implemented coherence schemes are crucial to obtaining good performance. We also developed optimizations applicable to general OpenMP implementations, namely redundant barrier removal and privatization of dynamically allocated objects. Experimental results for the coherency optimization show that aggressive compiler optimizations are quite effective for a shared-write intensive program because the coherence-induced communication volume in such a program is much larger than that in shared-read intensive programs.
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Klimczyk, Witold Artur, i Zdobyslaw Jan Goraj. "Robust design and optimization of UAV empennage". Aircraft Engineering and Aerospace Technology 89, nr 4 (3.07.2017): 609–19. http://dx.doi.org/10.1108/aeat-11-2016-0221.
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Purpose This paper aims to address the issue of designing aerodynamically robust empennage. Aircraft design optimization often narrowed to analysis of cruise conditions does not take into account other flight phases (manoeuvres). These, especially in unmanned air vehicle sector, can be significant part of the whole flight. Empennage is a part of the aircraft, with crucial function for manoeuvres. It is important to consider robustness for highest performance. Design/methodology/approach Methodology for robust wing design is presented. Surrogate modelling using kriging is used to reduce the optimization cost for high-fidelity aerodynamic calculations. Analysis of varying flight conditions, angle of attack, is made to assess robustness of design for particular mission. Two cases are compared: global optimization of 11 parameters and optimization divided into two consecutive sub-optimizations. Findings Surrogate modelling proves its usefulness for cutting computational time. Optimum design found by splitting problem into sub-optimizations finds better design at lower computational cost. Practical implications It is demonstrated, how surrogate modelling can be used for analysis of robustness, and why it is important to consider it. Intuitive split of wing design into airfoil and planform sub-optimizations brings promising savings in the optimization cost. Originality/value Methodology presented in this paper can be used in various optimization problems, especially those involving expensive computations and requiring top quality design.
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Zhang, Zhengfang, Weifeng Chen i Xiaoliang Cheng. "A Penalty Optimization Algorithm for Eigenmode Optimization Problem Using Sensitivity Analysis". Communications in Computational Physics 15, nr 3 (marzec 2014): 776–96. http://dx.doi.org/10.4208/cicp.190313.090913a.
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AbstractThis paper investigates the eigenmode optimization problem governed by the scalar Helmholtz equation in continuum system in which the computed eigenmode approaches the prescribed eigenmode in the whole domain. The first variation for the eigenmode optimization problem is evaluated by the quadratic penalty method, the adjoint variable method, and the formula based on sensitivity analysis. A penalty optimization algorithm is proposed, in which the density evolution is accomplished by introducing an artificial time term and solving an additional ordinary differential equation. The validity of the presented algorithm is confirmed by numerical results of the first and second eigenmode optimizations in 1Dand 2Dproblems.
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Mueller, Carsten. "Multi-Objective Optimization of Software Architectures Using Ant Colony Optimization". Lecture Notes on Software Engineering 2, nr 4 (2014): 371–74. http://dx.doi.org/10.7763/lnse.2014.v2.152.
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Hegazy, Tarek, i Ahmed Elhakeem. "Multiple optimization and segmentation technique (MOST) for large-scale bilevel life cycle optimization". Canadian Journal of Civil Engineering 38, nr 3 (marzec 2011): 263–71. http://dx.doi.org/10.1139/l10-134.
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This paper introduces a new formulation for large-scale combinatorial bilevel optimization problems that involve integer, discrete, two-level decisions. The most vivid example where the new technique most applies is the life cycle optimization needed to allocate repair types and repair timings to a number of infrastructure assets (e.g., building components). Combining these decisions into a single optimization for hundreds of assets simultaneously makes the optimization problem complex and prohibitive. For such a large-scale problem, a multiple optimization and segmentation technique (MOST) is proposed to handle the optimization one level at a time through a series of small-size optimizations that can be solved easily. The performance of MOST has been validated on various problem sizes and proved to be innovative and can handle thousands of variables simultaneously.
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Gu, Xiaoyu, John E. Renaud, Leah M. Ashe, Stephen M. Batill, Amrjit S. Budhiraja i Lee J. Krajewski. "Decision-Based Collaborative Optimization". Journal of Mechanical Design 124, nr 1 (1.08.2000): 1–13. http://dx.doi.org/10.1115/1.1432991.
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In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg [1,2] is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.
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Bendsoe, Martin P. "Multidisciplinary Topology Optimization". Proceedings of The Computational Mechanics Conference 2006.19 (2006): 1. http://dx.doi.org/10.1299/jsmecmd.2006.19.1.
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Phommixay, Sengthavy, Mamadou Lamine Doumbia i David Lupien St-Pierre. "Review on the cost optimization of microgrids via particle swarm optimization". International Journal of Energy and Environmental Engineering 11, nr 1 (27.12.2019): 73–89. http://dx.doi.org/10.1007/s40095-019-00332-1.
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AbstractEconomic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated.
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Lee, Youngmyung, Yong-Ha Han, Sang-ok Park i Gyung-Jin Park. "Vehicle crash optimization considering a roof crush test and a side impact test". Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, nr 10 (5.09.2018): 2455–66. http://dx.doi.org/10.1177/0954407018794259.
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The vehicle performances for the side impact test and the roof crush test are dependent on the side structure design of a vehicle. Crash optimization can be employed to enhance the performances. A meta-model-based structural optimization technique is generally utilized in the optimization process since the technique is simple to use. However, the meta-model-based optimization is not suitable for problems with many design variables such as topology and topometry optimizations. A crash optimization methodology is proposed to consider both the side impact test and the roof crush test. The equivalent static loads method is adopted for the side impact test and the enforced displacement method is adopted for the roof crush test, and the two methods are integrated. A design formulation is defined. The survival distance from the side impact test and the roof strength for the roof crush test are used for the design constraints. Crash optimization is performed for a practical large-scale structure. For conceptual design, reinforcement of the B-pillar is determined by using topometry optimization, and size and shape optimizations are employed for a detailed design to satisfy the design constraints while the mass is reduced.
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Huang, Daniel, Junwei Lucas Bao i Jean-Baptiste Tristan. "Geometry meta-optimization". Journal of Chemical Physics 156, nr 13 (7.04.2022): 134109. http://dx.doi.org/10.1063/5.0087165.
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Recent work has demonstrated the promise of using machine-learned surrogates, in particular, Gaussian process (GP) surrogates, in reducing the number of electronic structure calculations (ESCs) needed to perform surrogate model based (SMB) geometry optimization. In this paper, we study geometry meta-optimization with GP surrogates where a SMB optimizer additionally learns from its past “experience” performing geometry optimization. To validate this idea, we start with the simplest setting where a geometry meta-optimizer learns from previous optimizations of the same molecule with different initial-guess geometries. We give empirical evidence that geometry meta-optimization with GP surrogates is effective and requires less tuning compared to SMB optimization with GP surrogates on the ANI-1 dataset of off-equilibrium initial structures of small organic molecules. Unlike SMB optimization where a surrogate should be immediately useful for optimizing a given geometry, a surrogate in geometry meta-optimization has more flexibility because it can distribute its ESC savings across a set of geometries. Indeed, we find that GP surrogates that preserve rotational invariance provide increased marginal ESC savings across geometries. As a more stringent test, we also apply geometry meta-optimization to conformational search on a hand-constructed dataset of hydrocarbons and alcohols. We observe that while SMB optimization and geometry meta-optimization do save on ESCs, they also tend to miss higher energy conformers compared to standard geometry optimization. We believe that further research into characterizing the divergence between GP surrogates and potential energy surfaces is critical not only for advancing geometry meta-optimization but also for exploring the potential of machine-learned surrogates in geometry optimization in general.
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Sumarno, Radiktyo Nindyo, Susatyo Handoko i Mochammad Facta. "The Improvement of Electric Power Losses Using Bank Capacitor and Tap Changer With Shark Smell Algorithm". TEKNIK 41, nr 3 (9.09.2020): 212–18. http://dx.doi.org/10.14710/teknik.v41i3.24818.
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One way to optimize the transmission line is to reduce electrical power losses. Tap changers on power transformers and bank capacitors can be used to regulate the system voltage resulting in lower power losses in the transmission line. Determining the value of tap settings and bank capacitors in the planning process is challenging to do with certainty. It is generally carried out through a trial and error mechanism using the power flow method. Since the determination of tap settings and bank capacitors values is difficult to do with certainty, this research was carried out with optimization with the shark smell algorithm. Such optimization aims to get a more appropriate tap changer and capacitor bank change values on the IEEE 30-bus system. In this study, several optimizations were carried out, namely optimization of tap settings, optimization of bank capacitors, and tap setting optimization combined with bank capacitors' optimization. Conducting tap setting optimization, we obtained an active power loss of 0.65% from the condition without optimization. In optimizing bank capacitors, we reduce active power losses of 0.90% compared to conditions without optimization. In optimizing the combination of tap setting and bank capacitors, the active power losses are reduced by 1.23%. Comparing the results of all these optimizations shows that the combination of bank tap setting and capacitor optimization is obtained by reducing the most active power losses. In this study, the reduction of active power losses resulted in 217.2 kW. The results show that the Shark Smell algorithm can provide better optimization results of 1.23% compared to conditions without optimization based on the test value.
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Zhang, Zhuo Qun, i Hong Nan Li. "Two-Level Optimization Method of Transmission Tower Structure Based on Ant Colony Algorithm". Advanced Materials Research 243-249 (maj 2011): 5849–53. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.5849.
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This paper highlights a two-level optimization process combined with ant colony optimization algorithm (ACO)，which is applied to design the shape of the transmission tower structure, and compiles optimization design procedure for the solution of the structure shape with the MATLAB language. By comparing the different methods of the transmission tower structure optimization design, the ACO algorithm is demonstrated to be more accuracy and practical in dealing the complex optimizations problem, i.e. the combination of continuous variables and discrete variables. These calculation examples illustrate that the two-level optimization method is reliable and versatile.
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Tao, Ziyou. "Stress Analysis and Size Optimization of Suspension Beam Structure of Robot Manipulator". Theoretical and Natural Science 2, nr 1 (20.02.2023): 92–96. http://dx.doi.org/10.54254/2753-8818/2/20220174.
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In this paper, the stress and deformation of a manipulator structure are analyzed, and the structural optimization design is carried out. The initial configuration is a cantilever beam structure with rectangular section, which is fixed at one end and bears a load of 1 ton at the other end. After stress and deformation analysis with ABAQUS software and SolidWorks software, three optimizations were carried out. Geometric configuration optimization, topology optimization and material optimization. After optimization, the overall quality of the structure is reduced by 80%, and there is no great loss of strength and safety.
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Wu, Qingkun, Lingen Chen, Yanlin Ge i Huijun Feng. "Four-Objective Optimization of an Irreversible Magnetohydrodynamic Cycle". Entropy 24, nr 10 (14.10.2022): 1470. http://dx.doi.org/10.3390/e24101470.
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Based on the existing model of an irreversible magnetohydrodynamic cycle, this paper uses finite time thermodynamic theory and multi-objective genetic algorithm (NSGA-II), introduces heat exchanger thermal conductance distribution and isentropic temperature ratio of working fluid as optimization variables, and takes power output, efficiency, ecological function, and power density as objective functions to carry out multi-objective optimization with different objective function combinations, and contrast optimization results with three decision-making approaches of LINMAP, TOPSIS, and Shannon Entropy. The results indicate that in the condition of constant gas velocity, deviation indexes are 0.1764 acquired by LINMAP and TOPSIS approaches when four-objective optimization is performed, which is less than that (0.1940) of the Shannon Entropy approach and those (0.3560, 0.7693, 0.2599, 0.1940) for four single-objective optimizations of maximum power output, efficiency, ecological function, and power density, respectively. In the condition of constant Mach number, deviation indexes are 0.1767 acquired by LINMAP and TOPSIS when four-objective optimization is performed, which is less than that (0.1950) of the Shannon Entropy approach and those (0.3600, 0.7630, 0.2637, 0.1949) for four single-objective optimizations, respectively. This indicates that the multi-objective optimization result is preferable to any single-objective optimization result.
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Xu, Haoran, Lingen Chen, Yanlin Ge i Huijun Feng. "Four-Objective Optimization of an Irreversible Stirling Heat Engine with Linear Phenomenological Heat-Transfer Law". Entropy 24, nr 10 (19.10.2022): 1491. http://dx.doi.org/10.3390/e24101491.
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This paper combines the mechanical efficiency theory and finite time thermodynamic theory to perform optimization on an irreversible Stirling heat-engine cycle, in which heat transfer between working fluid and heat reservoir obeys linear phenomenological heat-transfer law. There are mechanical losses, as well as heat leakage, thermal resistance, and regeneration loss. We treated temperature ratio x of working fluid and volume compression ratio λ as optimization variables, and used the NSGA-II algorithm to carry out multi-objective optimization on four optimization objectives, namely, dimensionless shaft power output P¯s, braking thermal efficiency ηs, dimensionless efficient power E¯p and dimensionless power density P¯d. The optimal solutions of four-, three-, two-, and single-objective optimizations are reached by selecting the minimum deviation indexes D with the three decision-making strategies, namely, TOPSIS, LINMAP, and Shannon Entropy. The optimization results show that the D reached by TOPSIS and LINMAP strategies are both 0.1683 and better than the Shannon Entropy strategy for four-objective optimization, while the Ds reached for single-objective optimizations at maximum P¯s, ηs, E¯p, and P¯d conditions are 0.1978, 0.8624, 0.3319, and 0.3032, which are all bigger than 0.1683. This indicates that multi-objective optimization results are better when choosing appropriate decision-making strategies.
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Xie, Hua Long, Hui Min Guo, Qing Bao Wang i Yong Xian Liu. "The Spindle Structural Optimization Design of HTC3250µn NC Machine Tool Based on ANSYS". Advanced Materials Research 457-458 (styczeń 2012): 60–64. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.60.
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The optimization of spindle has important significance. The optimization method based on ANSYS is introduced and spindle mathematical mode of HTC3250µn NC machine tool is given. By scanning of design variables, the main optimized design variables are determined. The single objective and multi-objective optimizations are done. In the end, the main size comparison of spindle before and after optimization is given.
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Liu, Yuanyuan, Shunguang Song i Chunjie Wang. "Multi-objective optimization on the shock absorber design for the lunar probe using nondominated sorting genetic algorithm II". International Journal of Advanced Robotic Systems 14, nr 4 (1.07.2017): 172988141772046. http://dx.doi.org/10.1177/1729881417720467.
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In this article, the design on the shock absorber of the lunar probe soft landing can be considered as a single- or multi-objective optimization problem. Here, the optimized objective parameters include the maximum toppling stability, defined as Dmin, and the minimum stroke of primary strut energy absorption, SPmax. However, the two optimized variables are conflict objectives. In order to give an overall consideration about the multi-performances of landing, the multi-objective optimization strategy is proposed and nondominated sorting genetic algorithm II is employed to find the best decision parameters of the shock absorber design. To conduct the optimizations, firstly, the worst landing cases and safety boundaries for both toppling and primary strut energy absorptions are obtained by the computer simulation experiments. Both single- and multi-objective optimizations are then implemented aiming to expand the stability boundary. The results show that the landing stability is effectively improved after optimizations, and also demonstrate that the multi-objective optimization strategy is superior to that of the single-objective optimization.
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He, Jinhu, Lingen Chen, Yanlin Ge, Shuangshuang Shi i Fang Li. "Four-Objective Optimizations of a Single Resonance Energy Selective Electron Refrigerator". Entropy 24, nr 10 (11.10.2022): 1445. http://dx.doi.org/10.3390/e24101445.
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According to the established model of a single resonance energy selective electron refrigerator with heat leakage in the previous literature, this paper performs multi-objective optimization with finite-time thermodynamic theory and NSGA-II algorithm. Cooling load (R¯), coefficient of performance (ε), ecological function (ECO¯), and figure of merit (χ¯) of the ESER are taken as objective functions. Energy boundary (E′/kB) and resonance width (ΔE/kB) are regarded as optimization variables and their optimal intervals are obtained. The optimal solutions of quadru-, tri-, bi-, and single-objective optimizations are obtained by selecting the minimum deviation indices with three approaches of TOPSIS, LINMAP, and Shannon Entropy; the smaller the value of deviation index, the better the result. The results show that values of E′/kB and ΔE/kB are closely related to the values of the four optimization objectives; selecting the appropriate values of the system can design the system for optimal performance. The deviation indices are 0.0812 with LINMAP and TOPSIS approaches for four-objective optimization (ECO¯−R¯−ε−χ¯), while the deviation indices are 0.1085, 0.8455, 0.1865, and 0.1780 for four single-objective optimizations of maximum ECO¯, R¯, ε, and χ¯, respectively. Compared with single-objective optimization, four-objective optimization can better take different optimization objectives into account by choosing appropriate decision-making approaches. The optimal values of E′/kB and ΔE/kB range mainly from 12 to 13, and 1.5 to 2.5, respectively, for the four-objective optimization.
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Vlahopoulos, N., i C. G. Hart. "A Multidisciplinary Design Optimization Approach to Relating Affordability and Performance in a Conceptual Submarine Design". Journal of Ship Production and Design 26, nr 04 (1.11.2010): 273–89. http://dx.doi.org/10.5957/jspd.2010.26.4.273.
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A multidisciplinary design optimization (MDO) framework is used for a conceptual submarine design study. Four discipline-level performances—internal deck area, powering, maneuvering, and structural analysis—are optimized simultaneously. The four discipline-level optimizations are driven by a system level optimization that minimizes the manufacturing cost while at the same time coordinates the exchange of information and the interaction among the discipline-level optimizations. Thus, the interaction among individual optimizations is captured along with the impact of the physical characteristics of the design on the manufacturing cost. A geometric model for the internal deck area of a submarine is created, and resistance, structural design, and maneuvering models are adapted from theoretical information available in the literature. These models are employed as simulation drivers in the discipline-level optimizations. Commercial cost-estimating software is leveraged to create a sophisticated, automated affordability model for the fabrication of a submarine pressure hull at the system level. First, each one of the four discipline optimizations and also the cost-related top level optimization are performed independently. As expected, five different design configurations result, one from each analysis. These results represent the "best" solution from each individual discipline optimization, and they are used as reference for comparison with the MDO solution. The deck area, resistance, structural, maneuvering, and affordability models are then synthesized into a multidisciplinary optimization statement reflecting a conceptual submarine design problem. The results from this coordinated MDO capture the interaction among disciplines and demonstrate the value that the MDO system offers in consolidating the results to a single design that improves the discipline-level objective functions while at the same time produces the highest possible improvement at the system level.
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Onaolapo, A. K., i B. T. Abe. "An Extensive Assessment of the Energy Management and Design of Battery Energy Storage in Renewable Energy Systems". WSEAS TRANSACTIONS ON POWER SYSTEMS 19 (9.05.2024): 146–70. http://dx.doi.org/10.37394/232016.2024.19.17.
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Many benefits are derivable when renewable energy systems (RES) are integrated with battery energy storage systems (BESS). However, appropriate energy management techniques should be adopted to realize optimal benefits. Many BESS operations’ optimization approaches are available in RES with various techno-economic, environmental, and dispatch-related outputs. BESS operations are optimized using different methods. Past studies have mainly concentrated on certain renewable energy systems designed for specific purposes, such as distributed generation or large-scale. This paper thoroughly examines and analyzes various battery management systems by considering the relationship between the optimization methodology and the intended application. This strategy enables the identification of connections between favored optimization approaches and specific optimization goals. Some approaches are more effective in solving economic goal optimizations, whereas others are commonly used for technical goal optimizations. The selection of the solution methodology is also demonstrated to be highly contingent upon the degree of mathematical formulation of the problem. An analysis is conducted to assess the strengths and limitations of the described optimization techniques. The conclusion is that hybrid approaches, which combine the benefits of multiple techniques, will significantly impact the creation of future operating strategies. This paper provides a comprehensive analysis of optimization approaches and battery applications, aiming to assist researchers in efficiently identifying appropriate optimization strategies for emerging applications in the new generation.
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Liu, Manqi. "Current Status, Development, And Application of Optimization Methods for Analog Circuits". Highlights in Science, Engineering and Technology 81 (26.01.2024): 438–43. http://dx.doi.org/10.54097/j4vjfn83.
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The optimization of analog circuits has always relied on the experience and intuition of engineers to find suitable parameters to meet the requirements of the circuit, which is time-consuming and costly. This paper outlines and analyzes the optimization methods for analog circuits in recent years, and draws some summaries that can be used as references for subsequent optimization circuits. The optimization of analog circuits is mainly divided into the optimization of the performance of the hardware in the circuit and the optimization of the circuit structure. Hardware optimization, this paper mainly focuses on the optimization of diodes, transistors, field effect tubes and operational amplifiers, the four main hardware optimizations, from the material, structure, production process and other aspects of the analysis. For the optimization of circuit structure, this paper mainly analyzes from the aspects of algorithms, mathematical models, and the use of integrated methods. The significance of these optimization methods and the outstanding advantages and main application directions of different methods are further explained. The search for optimization methods can reduce the high time cost of analog circuit optimization that is purely dependent on experience and intuition, and is significant in increasing efficiency.
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Teng Fei, Teng Fei. "Research on Improved Ant Colony Optimization Based on Adaptive Chemical Reaction Optimization". 電腦學刊 32, nr 4 (sierpień 2021): 166–78. http://dx.doi.org/10.53106/199115992021083204013.
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Cui-Cui Cai, Cui-Cui Cai, Mao-Sheng Fu Cui-Cui Cai, Xian-Meng Meng Mao-Sheng Fu, Qi-Jian Wang Xian-Meng Meng i Yue-Qin Wang Qi-Jian Wang. "Modified Harris Hawks Optimization Algorithm with Multi-strategy for Global Optimization Problem". 電腦學刊 34, nr 6 (grudzień 2023): 091–105. http://dx.doi.org/10.53106/199115992023123406007.
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<p>As a novel metaheuristic algorithm, the Harris Hawks Optimization (HHO) algorithm has excellent search capability. Similar to other metaheuristic algorithms, the HHO algorithm has low convergence accuracy and easily traps in local optimal when dealing with complex optimization problems. A modified Harris Hawks optimization (MHHO) algorithm with multiple strategies is presented to overcome this defect. First, chaotic mapping is used for population initialization to select an appropriate initiation position. Then, a novel nonlinear escape energy update strategy is presented to control the transformation of the algorithm phase. Finally, a nonlinear control strategy is implemented to further improve the algorithm’s efficiency. The experimental results on benchmark functions indicate that the performance of the MHHO algorithm outperforms other algorithms. In addition, to validate the performance of the MHHO algorithm in solving engineering problems, the proposed algorithm is applied to an indoor visible light positioning system, and the results show that the high precision positioning of the MHHO algorithm is obtained.</p> <p> </p>
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Gubanov, Oleg, i Luca Cortelezzi. "On the cost efficiency of mixing optimization". Journal of Fluid Mechanics 692 (16.12.2011): 112–36. http://dx.doi.org/10.1017/jfm.2011.498.
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AbstractIn this study we discuss the cost efficiency of the optimization of a new prototypical mixing flow, the Fourier sine flow, an extension of the sine flow. The Fourier sine flow stirs a mixture on a two-dimensional torus by blinking, at prescribed switching times, two orthogonal velocity fields with profiles represented by a Fourier sine series. We derive a family of mixers of increasing complexity by truncating the series to one, two, three and four modes. We consider the optimization of the velocity profiles and the optimization of the stirring protocol. We implement the former by computing, at each iteration, the amplitudes and phase shifts of the Fourier modes synthesizing the velocity profiles that minimize the mix-norm, our cost function, i.e. maximize the quality of mixing. We implement the latter by selecting, at each iteration, the best performing of the two orthogonal stirring velocity fields, i.e. the velocity field that minimizes the mix-norm. To obtain a physically meaningful optimization problem, we constrain the kinetic energy of the flow to be the same among all mixers and use the viscous dissipation as an estimate of the power input needed to operate the mixers. We characterize the performance of the mixers using three cost functions: the homogenization time, the computational cost of optimization and the total energy consumption. We test the mixers on a range of admissible power inputs using two representative switching times. We report some surprising results. Mixers equipped with the velocity profile optimization and a periodic stirring protocol cannot be optimal, i.e. their performance depends on the switching time chosen, independently of the number of Fourier modes used in the optimization. Apparently, optimal mixers can be obtained only by coupling velocity profile and stirring protocol optimizations. The computational cost of the optimization depends only on the number of Fourier modes used and grows by about an order of magnitude for each Fourier mode added to the optimization. At low power inputs, the coupled optimizations allow us to obtain an attractive reduction of the homogenization time in combination with a reduction of the total energy required to produce it. However, increasing the power input does not guarantee a reduction of the homogenization time. Counter-intuitively, there are ranges of power inputs for which both the homogenization time and the total energy increase when increasing the power input. Finally, for large enough power inputs, optimizations with two, three and four Fourier modes perform similarly, making the former optimization the most cost-efficient.
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Wang, Chenxiao, Zach Arani, Le Gruenwald, Laurent d'Orazio i Eleazar Leal. "Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning". International Journal of Database Management Systems 13, nr 1 (28.02.2021): 21–40. http://dx.doi.org/10.5121/ijdms.2021.13102.
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In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.
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Jia, Jia, i Dejun Mu. "Low-Energy-Orientated Resource Scheduling in Cloud Computing by Particle Swarm Optimization". Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, nr 2 (kwiecień 2018): 339–44. http://dx.doi.org/10.1051/jnwpu/20183620339.
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In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in this algorithm obtains its Pareto local optimization. After the assembly of local optimizations, the algorithm generates the Pareto global optimization for one server plan. The final solution to our problem is the optimal one among all server plans. Experimental results show the good performance of the proposed method. Comparing with the widely-used Round robin scheduling method, the proposed method requires only 45.5% dynamic energy cost.
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Nowak, M. "Improved aeroelastic design through structural optimization". Bulletin of the Polish Academy of Sciences: Technical Sciences 60, nr 2 (1.10.2012): 237–40. http://dx.doi.org/10.2478/v10175-012-0031-8.
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Abstract. The paper presents the idea of coupled multiphysics computations. It shows the concept and presents some preliminary results of static coupling of structural and fluid flow codes as well as biomimetic structural optimization. The model for the biomimetic optimization procedure was the biological phenomenon of trabecular bone functional adaptation. Thus, the presented structural bio-inspired optimization system is based on the principle of constant strain energy density on the surface of the structure. When the aeroelastic reactions are considered, such approach allows fulfilling the mechanical theorem for the stiffest design, comprising the optimizations of size, shape and topology of the internal structure of the wing.
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Kota, László, i Károly Jármai. "Improving optimization using adaptive algorithms". Pollack Periodica 16, nr 1 (25.03.2021): 14–18. http://dx.doi.org/10.1556/606.2020.00180.
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AbstractIn the research projects and industrial projects severe optimization problems can be met, where the number of variables is high, there are a lot of constraints, and they are highly nonlinear and mostly discrete issues, where the running time can be calculated sometimes in weeks with the usual optimization methods on an average computer. In most cases in the logistics industry, the most robust constraint is the time. The optimizations are running on a typical office configuration, and the company accepts the suboptimal solution what the optimization method gives within the appropriate time limit. That is, why adaptivity is needed. The adaptivity of the optimization technique includes parameters of fine-tuning. On this way, the most sensitive setting can be found. In this article, some additional adaptive methods for logistic problems have been investigated to increase the effectivity, improve the solution in a strict time condition.
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Feng, Huijun, Wei Tang, Lingen Chen, Junchao Shi i Zhixiang Wu. "Multi-Objective Constructal Optimization for Marine Condensers". Energies 14, nr 17 (5.09.2021): 5545. http://dx.doi.org/10.3390/en14175545.
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A marine condenser with exhausted steam as the working fluid is researched in this paper. Constructal designs of the condenser are numerically conducted based on single and multi-objective optimizations, respectively. In the single objective optimization, there is an optimal dimensionless tube diameter leading to the minimum total pumping power required by the condenser. After constructal optimization, the total pumping power is decreased by 42.3%. In addition, with the increase in mass flow rate of the steam and heat transfer area and the decrease in total heat transfer rate, the minimum total pumping power required by the condenser decreases. In the multi-objective optimization, the Pareto optimal set of the entropy generation rate and total pumping power is gained. The optimal results gained by three decision methods in the Pareto optimal set and single objective optimizations are compared by the deviation index. The optimal construct gained by the TOPSIS decision method corresponding to the smallest deviation index is recommended in the optimal design of the condenser. These research ideas can also be used to design other heat transfer devices.
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VLAD, Monica, i Octavian-Thor PLETER. "AEROSPACE PERFORMANCE FACTOR OPTIMIZATION". Review of the Air Force Academy 13, nr 3 (16.12.2015): 101–6. http://dx.doi.org/10.19062/1842-9238.2015.13.3.17.
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Salman, Abbas Musleh, i Ahmed Sabah Al-Jilawi. "Combinatorial Optimization and Nonlinear Optimization". Journal of Physics: Conference Series 1818, nr 1 (1.03.2021): 012134. http://dx.doi.org/10.1088/1742-6596/1818/1/012134.
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KAGAWA, Susumu. "Sub-optimization and Total Optimization". Journal of The Institute of Electrical Engineers of Japan 134, nr 12 (2014): 803. http://dx.doi.org/10.1541/ieejjournal.134.803.
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Qi, Liqun, Li-Zhi Liao, Wenan Zang i Guanglu Zhou. "Continuous optimization and combinatorial optimization". Frontiers of Mathematics in China 5, nr 1 (28.10.2009): 1–2. http://dx.doi.org/10.1007/s11464-009-0044-2.
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Zhang, Huan, i Pubudu N. Pathirana. "Optimization-based formation of autonomous mobile robots". Robotica 29, nr 4 (5.08.2010): 515–25. http://dx.doi.org/10.1017/s0263574710000366.
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SUMMARYThe formation of autonomous mobile robots to an arbitrary geometric pattern in a distributed fashion is a fundamental problem in formation control. This paper presents a new asynchronous, memoryless (oblivious) algorithm to the formation problem via distributed optimization techniques. The optimization minimizes an appropriately defined difference function between the current robot distribution and the target geometric pattern. The optimization processes are performed independently by individual robots in their local coordinate systems. A movement strategy derived from the results of the distributed optimizations guarantees that every movement makes the current robot configuration approaches the target geometric pattern until the final pattern is reached.
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Gireesha. B, Mr, i . "A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques". International Journal of Engineering & Technology 7, nr 4.5 (22.09.2018): 455. http://dx.doi.org/10.14419/ijet.v7i4.5.20205.
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From few decades’ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle Swarm Optimization (PSO) is presented and also the most suitable fitness functions and its numerical expressions have discussed.
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Yepes, Víctor, José V. Martí i José García. "Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls". Sustainability 12, nr 7 (1.04.2020): 2767. http://dx.doi.org/10.3390/su12072767.
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The optimization of the cost and CO 2 emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min–max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO 2 emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.
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Brekalo, Sanja, Klaudio Pap i Bruno Trstenjak. "Enhancing Rendering Performance in Complex Visualizations by using Optimization Techniques and Algorithms in Browser Environments". Engineering, Technology & Applied Science Research 14, nr 3 (1.06.2024): 14049–55. http://dx.doi.org/10.48084/etasr.7201.
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This research is based on the hypothesis that optimization techniques can significantly improve the performance of complex visualizations in web browsers. The aim of the former was to determine to which extent the optimization can be achieved. Optimizations were coded to improve visualization, reduce the need for visual rendering, and decrease script execution time as well as the needed resources. To test the hypothesis, various optimization methods and algorithms were implemented on the initial visualization script and were tested. The main goal of this implementation was to assess how optimization methods, including quadtrees, spatial hashing, binning, LOD adjustments, and the use of the map data structure, affect the performance of web visualization. The obtained results confirmed the hypothesis and the original animation was significantly improved. The implementation of optimizations had a positive effect on the performance of visualizations. The conducted tests gave concrete evidence confirming the validity of the initial hypothesis. This led to certain conclusions regarding which methods provide the best results when optimizing complex visualizations. Key recommendations for code optimization, which can be used in the development of complex visualizations in web browsers, were derived.
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Wang, Jin Yang, Guo Min Cui i Fu Yu Peng. "Integration of Distributing Energy System II: Optimization". Advanced Materials Research 512-515 (maj 2012): 1151–55. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.1151.
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The optimization of the Distributed Energy System can improve the total energy utilization efficiency greatly and achieve energy saving. Based on the digital simulation software introduced by Part Ⅰ, the optimizations of matching parameters and structure can be achieved. The example results showed that numerical simulation can realize the optimization of matching parameters and economic operation parameters for the parts and the whole system, and the design result with the highest efficiency is obtained.
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Delinchant, Benoit, Frédéric Wurtz, João Vasconcelos i Jean-Louis Coulomb. "Framework for the optimization of online computable models". COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, nr 3 (29.04.2014): 745–58. http://dx.doi.org/10.1108/compel-10-2012-0211.
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Purpose – The purpose of this paper is to make easily accessible models to test and compare the optimization algorithms we develop. Design/methodology/approach – For this, the paper proposes an optimization framework based on software component, web service, and plugin to exploit these models in different environments. Findings – The paper illustrates the discussion with optimizations in Matlab™ and R (www.r-project.org) of a transformer described and exploitable from the internet. Originality/value – The originality is to make easy implementation of simulation model and optimization algorithm coupling using software component, web service, and plugin.
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Rizky Amalia i Febriyanti Panjaitan. "Mask Detection Using Convolutional Neural Network Algorithm". Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, nr 4 (22.08.2022): 639–47. http://dx.doi.org/10.29207/resti.v6i4.4276.
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The World Health Organizations and the Ministry of Health of the Republic of Indonesia have required the use of masks to suppress the spread of COVID-19. WHO provides guidance on how to use a good mask to cover the mouth and nose. This study aims to detect the correct use of masks using the Convolutional Neural Network. CNN is a popular Deep Learning algorithm for image data classification problems. The Mask Usage Detector is built with the help of a pre-trained MobileNetV2 model with an architecture that supports media that has minimum computations. This study will also compare the performance of three optimization methods from CNN, namely Adam, SGD, and RMSprop in detecting the use of masks. Performance will be seen from the test results by analyzing the values of accuracy, precision, and recall. The dataset used is in the form of image data of 2,029 images for 2 categories, namely "masked" and "unmasked". A total of 1,623 images were used as training data and 406 images for test data. Based on the testing process, the accuracy of each optimization is 93.84% with Adam optimization, 84.48% with SGD optimization, and 93.10% with RMSprop optimization. With the proposed model, this study obtains the performance results of the three CNN optimizations, and it is concluded that adam's optimization gives better performance results than the other two optimizations.