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Multicanonical basin hopping: A new global optimization method for complex systems

JOURNAL OF CHEMICAL PHYSICS 2004年第12期120卷 5536-5542页

作者： Zhan, LX Piwowar, B Liu, WK Hsu, PJ Lai, SK Chen, JZY Univ Waterloo Dept Phys Waterloo ON N2L 3G1 Canada Natl Cent Univ Dept Phys Chungli 320 Taiwan

We introduce a new optimization algorithm that combines the basin-hopping method, which can be used to efficiently map out an energy landscape associated with minima, with the multicanonical Monte Carlo method, which encourages the system to move out of energy traps during the computation. As an example of implementing the algorithm for the global minimization of a multivariable system, we consider the Lennard-Jones systems containing 150-185 particles, and find that the new algorithm is more efficient than the original basin-hopping method. (C) 2004 American Institute of Physics.

关键词： Complex systems optimization algorithms Energy sources Global optimization optimization method ENERGY TRAPPING

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Moth flame algorithm-based optimization of a reduced switch multilevel inverter topology suitable for standalone application

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NEURAL COMPUTING & APPLICATIONS 2024年第16期36卷 9437-9479页

作者： Shanono, Ibrahim Haruna Abdullah, Nor Rul Hasma Daniyal, Hamdan Muhammad, Aisha Bayero Univ Fac Engn Elect Dept Kano Nigeria Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia Bayero Univ Fac Engn Dept Mechatron Kano Nigeria

The escalating demand for sustainable and environmentally friendly energy sources has driven substantial growth in renewable energy adoption across residential and industrial sectors. To effectively meet this demand, the development of efficient and sustainable renewable energy conversion methods is crucial, with inverters playing a pivotal role in achieving this objective. While researchers strive to enhance inverter power handling capabilities and reduce output harmonic contents, the incorporation of additional power electronic switches and peripheral devices presents challenges such as increased circuit cost, complexity, and size. Additionally, the utilization of high-frequency switching techniques for achieving low output harmonics results in elevated switching losses and electromagnetic interference, adversely affecting sensitive electronic devices. This research introduces a novel approach to address these issues through the introduction of a reduced switch multilevel inverter topology. Unlike existing systems, the proposed topology employs a reduced number of power electronic switches and direct current sources to generate a stable output voltage waveform. Operating in a symmetric mode, the topology achieves a nine-level output voltage with enhanced harmonic elimination capabilities. A multiple-stepped selective harmonic elimination (SHE-PWM) switching control technique, employing a 1/3/3/1 distribution ratio, is utilized to extend the harmonic elimination range from 3 to 7 lower-order harmonics. To optimize the switching angles required for the proposed topology, the moth flame optimization (MFO) algorithm is employed and compared with particle swarm optimization (PSO) and whale optimization algorithms (WOA). The MFO algorithm exhibits faster convergence to the global optima, achieving an optimal fitness value of 3.322e-08 at 0.78 modulation points. This results in total harmonic distortion values of 0.7%, 0.757%, and 1.069% for MFO, PSO, and WOA, respective

关键词： optimization algorithms Total harmonic distortion Reduced device count Renewable energy Selective harmonic elimination Typhoon hardware-in-the-loop testing

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Computational algorithm for dynamic optimization of chemical vapor deposition processes in stagnation flow reactors

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2000年第7期147卷 2718-2726页

作者： Raja, LL Kee, RJ Serban, R Petzold, LR Colorado Sch Mines Div Engn Golden CO 80401 USA Univ Calif Santa Barbara Dept Mech & Environm Engn Computat Sci & Engn Program Santa Barbara CA 93106 USA

There are potentially great benefits to developing materials processes that deliberately vary process conditions such as temperature, or flow rates, during the course of the process. Transient processing holds the promise of reducing manufacturing cost and the possibility of producing material systems that would be infeasible to manufacture with steady processes. Once the notion of transient processing is embraced, there is a need and opportunity to develop optimal trajectories through which the process will proceed. In this paper, a stagnation flow dynamic optimization algorithm for two chemical vapor deposition processes is demonstrated. The first example seeks to control film composition during the deposition of yttrium-barium-copper oxide films, in which a wafer temperature transient is imposed. Transient trajectories of precursor flow rates are determined by optimization, so that the correct flux ratios of yttrium, barium, and copper atoms to the surface are maintained. The second example determines trajectories that minimize the cost associated with multiple competing objectives during the deposition of a copper film. Time varying trajectories of copper precursor concentration and the inlet flow velocity are computed so as to minimize a composite cost function that considers precursor utilization and process throughput. (C) 2000 The Electrochemical Society. S0013-4651(99)10-034-X. All rights reserved.

关键词： stagnation flow Current velocity optimization algorithms trajectory Flow rates materials handling Manufacturing costs Vapor deposition process Chemical vapor deposition deposition

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optimization of overlapping activities in the design phase of construction projects

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AUTOMATION IN CONSTRUCTION 2015年 59卷 81-95页

作者： Dehghan, Reza Hazini, Kamran Ruwanpura, Janaka Univ Calgary Ctr Project Management Excellence Schulich Sch Engn Calgary AB T2N 1N4 Canada

A well-known practice to accelerate construction projects is to overlap the design phase activities. For a typical construction project, a number of overlapping strategies exist during the design phase which all can result in timesaving. However, the cost of these strategies varies significantly depending on the total rework and complexity they generate. A favorable overlapping strategy is one that generates the required timesaving at the minimum cost. To find such a strategy, the question "Which activities have to be overlapped and to what extent to reduce the project duration at the minimum cost?" should be answered. This research aimed at answering the question through generating an overlapping optimization algorithm. The algorithm works based on the principles of genetic algorithms (GAs). The algorithm explained in the paper is unique compared to previous algorithms and frameworks available in the literature, as it can optimize multi-path networks and can handle all types of activity dependencies (i.e. finish-to-start, start-to-start, and finish-to-finish). It also takes both critical and non-critical activities into account and follows the critical path if the critical path changes or new critical paths emerge. A computer tool was also developed to run, examine and validate the overlapping optimization algorithm. This paper introduces the algorithm and the computer tool in detail and explains the results of their validation through optimizing a real-world project schedule. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Overlapping Fast-tracking optimization algorithms Time-cost trade-off Rework Construction management

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High-Dimensional Nonconvex Stochastic optimization by Doubly Stochastic Successive Convex Approximation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 6287-6302页

作者： Mokhtari, Aryan Koppel, Alec Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA US Army Computat & Informat Sci Directorate Res Lab Adelphi MD 20783 USA

In this paper, we consider supervised learning problems over training sets in which the number of training examples and the dimension of feature vectors are both large. We focus on the case where the loss function defining the quality of the parameter we wish to estimate may be non-convex, but also has a convex regularization. We propose a Doubly Stochastic Successive Convex approximation scheme (DSSC) able to handle non-convex regularized expected risk minimization. The method operates by decomposing the decision variable into blocks and operating on random subsets of blocks at each step (fusing the merits of stochastic approximation with block coordinate methods), and then implements successive convex approximation. In contrast to many stochastic convex methods whose almost sure behavior is not guaranteed in non-convex settings, DSSC attains almost sure convergence to a stationary solution of the problem. Moreover, we show that the proposed DSSC algorithm achieves stationarity at a rate of O((log t)/t(1/4)). Numerical experiments on a non-convex variant of a lasso regression problem show that DSSC performs favorably in this setting. We then apply this method to the task of dictionary learning from high-dimensional visual data collected from a ground robot, and observe reliable convergence behavior for a difficult non-convex stochastic program.

关键词： optimization algorithms stochastic optimization successive convex approximation

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Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm

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MATERIALS TESTING 2023年第12期65卷 1767-1775页

作者： Erdas, Mehmet Umut Kopar, Mehmet Yildiz, Betul Sultan Yildiz, Ali Riza Bursa Uludag Univ Dept Mech Engn TR-16059 Bursa Turkiye Bursa Uludag Univ Dept Automot Engn TR-16059 Bursa Turkiye

Nature-inspired metaheuristic algorithms are gaining popularity with their easy applicability and ability to avoid local optimum points, and they are spreading to wide application areas. Meta-heuristic optimization algorithms are used to achieve an optimum design in engineering problems aiming to obtain lightweight designs. In this article, structural optimization methods are used in the process of achieving the optimum design of a seat bracket. As a result of topology optimization, a new concept design of the bracket was created and used in shape optimization. In the shape optimization, the mass and stress values obtained depending on the variables, constraint, and objective functions were created by using artificial neural networks. The optimization problem based on mass minimization is solved by applying the dandelion optimization algorithm and verified by finite element analysis.

关键词： component design seat bracket artificial neural networks optimization algorithms

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High-quality fringe pattern generation based on binary pattern optimization with projector defocusing

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JOURNAL OF OPTICAL TECHNOLOGY 2017年第1期84卷 22-28页

作者： Li, X. -X. Zhang, Z. -J. Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai 200072 Peoples R China

Real-time three-dimensional reconstruction is increasingly important in many fields. However, it is a challenge for the conventional digital fringe projection technique. The binary defocusing technique applied to the digital fringe projection technique not only significantly improves the real-time performance but also fundamentally eliminates the nonlinearity of the projector. In the existing techniques, the dithering techniques based on optimization are superior to the others. However, those optimization methods have two obvious drawbacks: the objective function just qualifies the global intensity similarity while ignoring the local similarity, and the optimization framework is inefficient or time consuming. This paper first presents a novel objective function consisting of a global intensity term and a local structure term to comprehensively evaluate similarity. Second, a model optimization framework, which includes a hybrid optimization algorithm and a half period optimization idea, is employed. Both simulations and experimental results show the advantages of the proposed objective function and the optimization framework, as well as the improvement of quality and speed of 3D reconstruction. (C) 2017 Optical Society of America

关键词： interference pattern Projectors FRINGE PROJECTION defocusing Real time Pattern generation Technology Objective function optimization methods binary pattern optimization algorithms

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Buckling load of laminated composite plates using three variants of the biogeography-based optimization algorithm

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ACTA MECHANICA 2018年第4期229卷 1551-1566页

作者： Kaveh, A. Dadras, A. Malek, N. Geran Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Tehran 16 Iran

This paper presents the application of the biogeography-based optimization (BBO) and some of its variants in the optimization of stacking sequence of laminated composites. Harmony search is also implemented to compare its results with those of the BBO. The optimization objective is to maximize the buckling load of a symmetric and balanced laminated plate. Four laminated composites with different loadings and dimensions are studied, and the statistical comparison of the obtained configurations and buckling load capacities shows the high capability of the BBO with quadratic migration model in terms of robustness and global search.

关键词： CENTROSYMMETRIC optimization algorithms buckling load variants COMPOSITION BOARD Robustness laminate composite

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Modified Archimedes optimization algorithm for global optimization problems: a comparative study

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NEURAL COMPUTING & APPLICATIONS 2024年第14期36卷 8007-8038页

作者： Nurmuhammed, Mustafa Akdag, Ozan Karadag, Teoman Inonu Univ Malatya Malatya OIZ Vocat Sch Dept Elect & Energy Malatya Turkiye Malatya Turgut Ozal Univ Dept Comp Engn Malatya Turkiye Inonu Univ Dept Elect Elect Engn Malatya Turkiye

Archimedes optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes' Principle. In this study, a Modified Archimedes optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly optimization Algorithm, Particle Swarm optimization Butterfly optimization Algorithm, Golden Jackal optimization, Whale optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.

关键词： Benchmark functions Modified optimization algorithms optimization algorithms Swarm intelligence

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Photonic counterdiabatic quantum optimization algorithm

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COMMUNICATIONS PHYSICS 2024年第1期7卷 315页

作者： Chandarana, Pranav Paul, Koushik Garcia-de-Andoin, Mikel Ban, Yue Sanz, Mikel Chen, Xi Univ Basque Country UPV EHU Dept Phys Chem Apartado 644 Bilbao 48080 Spain Univ Basque Country UPV EHU EHU Quantum Ctr Barrio Sarriena S-N Leioa 48940 Spain TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Spain Univ Carlos III Madrid Dept Fis Avda Univ 30 Leganes 28911 Spain IKERBASQUE Basque Fdn Sci Bilbao 48009 Spain Basque Ctr Appl Math BCAM Bilbao 48009 Spain Inst Ciencia Mat Madrid CSIC E-28049 Madrid Spain

One of the key applications of near-term quantum computers has been the development of quantum optimization algorithms. However, these algorithms have largely been focused on qubit-based technologies. Here, we propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm reduces the required quantum operations for optimization compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization within the near-term era of quantum computing. Through illustrative benchmarking, we show that our approach can outperform existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct a proof-of-principle demonstration on Xanadu's eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm. The authors introduce a hybrid quantum-classical algorithm for photonic quantum computing that focuses on tackling continuous-variable optimization problems using fewer quantum operations than existing methods. The approach shows better performance and practical implementation potential, demonstrated on Xanadu's quantum chip.

关键词： optimization algorithms

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