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arXiv 2024年

作者： Jiang, Xiaotian Li, Jiaxiang Hong, Mingyi Zhang, Shuzhong Department of Industrial and System Engineering University of Minnesota MinneapolisMN United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States

In this paper, we consider bilevel optimization problem where the lower-level has coupled constraints, i.e. the constraints depend both on the upper- and lower-level variables. In particular, we consider two settings for the lower-level problem. The first is when the objective is strongly convex and the constraints are convex with respect to the lower-level variable;The second is when the lower-level is a linear program. We propose to utilize a barrier function reformulation to translate the problem into an unconstrained problem. By developing a series of new techniques, we proved that both the hyperfunction value and hypergradient of the barrier reformulated problem (uniformly) converge to those of the original problem under minimal assumptions. Further, to overcome the non-Lipschitz smoothness of hyperfunction and lower-level problem for barrier reformulated problems, we design an adaptive algorithm that ensures a non-asymptotic convergence guarantee. We also design an algorithm that converges to the stationary point of the original problem asymptotically under certain assumptions. The proposed algorithms require minimal assumptions, and to our knowledge, they are the first with convergence guarantees when the lower-level problem is a linear program. Numerical experiments are conducted to show the effectiveness of the proposed *** Codes 90C26 Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Parameter extraction from experimental PEFC data using an evolutionary optimization algorithm

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EUROPEAN PHYSICAL JOURNAL-APPLIED PHYSICS 2011年第2期54卷 23409-1-23409-10页

作者： Zaglio, M. Schuler, G. Wokaun, A. Mantzaras, J. Buechi, F. N. Paul Scherrer Inst Electrochem Lab CH-5232 Villigen Switzerland Paul Scherrer Inst Combust Res Lab CH-5232 Villigen Switzerland

The accurate characterization of the parameters related to the charge and water transport in the ionomer membrane of polymer electrolyte fuel cells (PEFC) is highly important for the understanding and interpretation of the overall cell behavior. Despite the big efforts to experimentally determine these parameters, a large scatter of data is reported in the literature, due to the inherent experimental difficulties. Likewise, the porosity and tortuosity of the gas diffusion layers affect the membrane water content and the local cell performance, but the published data are usually measured ex-situ, not accounting for the effect of clamping pressure. Using a quasi two-dimensional model and experimental current density data from a linear cell of technical size, a multiparameter optimization procedure based on an evolutionary algorithm has been applied to determine eight material properties highly influencing the cell performance. The optimization procedure converges towards a well defined solution and the resulting parameter values are compared to those available in the literature. The quality of the set of parameters extracted by the optimization procedure is assessed by a sensitivity analysis.

关键词： optimization Procedure moisture content Cells Diffusion layers Sensitivity analysis tortuosity optimization algorithms Parameter extraction evolutionary algorithm POLYMER ELECTROLYTE FUEL CELLS water absorption grip strength

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Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection Moth flame optimization algorithm Quantum computing Microarray data Cancer classification Bio-inspired algorithms Molecular biology optimization algorithms Evolutionary algorithms Swarm intelligence

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Active noise control of enclosed harmonic fields by using BEM-based optimization techniques

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APPLIED ACOUSTICS 1996年第1期48卷 15-32页

作者： Bai, MR Chang, S Department of Mechanical Engineering National Chiao Tung University 1001 Ta Hsueh Road Hsin Chu 30050 Taiwan

Boundary element methods (BEM) in conjunction with optimization algorithms are employed for active noise control of sound fields in three-dimensional enclosures. Total time-averaged acoustic potential energy is chosen as the cost function for the optimization procedure. The BEM is used to model the monochromatic sound field generated by noise sources in the enclosure. The constrained steepest descent method proves to be effective when both positions and amplitudes of the secondary sources are to be optimized, while the direct matrix inversion, the steepest descent method, and the conjugate gradient method prove to be useful when only the amplitudes of secondary sources are to be optimized. The developed BEM-based optimization techniques are applied to control the noise in a rectangular enclosure and a vehicle cabin.

关键词： boundary element method optimization algorithms enclosed harmonic fields active noise control

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MULTI-STRATEGY COEVOLVING AGING PARTICLE optimization

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2014年第1期24卷 1450008-1450026页

作者： Iacca, Giovanni Caraffini, Fabio Neri, Ferrante INCAS 3 Innovat Ctr Adv Sensors & Sensor Syst NL-9400 AT Assen Netherlands De Montfort Univ Sch Comp Sci & Informat CCI Leicester LE1 9BH Leics England

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

关键词： Swarm intelligence evolutionary computation optimization algorithms memetic computing robotic arms feedforward neural network

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Parametric Study and Multiobjective optimization of Fixed-Bed Fischer-Tropsch (FT) Reactor: The Improvement of FT Synthesis Product Formation and Synthetic Conversion

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2017年第34期56卷 9446-9466页

作者： Moazami, Nima Wyszynski, Miroslaw Lech Rahbar, Kiyarash Tsolakis, Athanasios Univ Birmingham Coll Engn & Phys Sci Dept Mech Engn Birmingham B15 2TT W Midlands England

A mathematical model of a fixed-bed reactor for Fischer-Tropsch synthesis (FTS) over 37% Co/SiO2 catalyst was developed to investigate the performance of the whole process for products' selectivity and syngas conversion. The model was capable of calculating the changes of reactant and products' concentrations, partial pressures, conversion, and selectivity. In a previous study, a series of combined novel FT and water gas shift (WGS) reaction mechanisms (eight elementary FT reaction pathways along with seven WGS kinetics models) were developed in order to calibrate and validate the mathematical model along with reaction kinetics at different experimental conditions. Such mathematical models with reaction networks can be used as a key tool to emphasize the most significant facts of FTS catalysis and chemistry. Integration of the global search optimization algorithm with the developed model was explained for estimation of kinetics parameters. Data analyses were carried out to ensure that the predicted model results as well as kinetic parameters are significantly relevant and physically meaningful. Parametric studies were performed to numerically investigate the effects of operating conditions (e.g., reaction temperature, total pressure, space velocity, and H-2/CO molar ratio) on products' selectivity and reactant conversion. These parameters were then included in a multiobjective optimization in MATLAB using NSGA-II to optimize the CO2 and HC products' selectivity and syngas conversion. The optimization process gives rise to a set of trade-off optimal solutions (Pareto-optimal solutions) which is used as a dynamic database depending on the specific requirement. A different operating condition can be selected from such a database which privileges the optimization of a particular output (e.g., conversion and selectivity).

关键词： selectivity optimization Procedure Parametric study optimization algorithms Fixed bed reactors Payment systems Dynamic databases Fourier transform spectrometry FISCHER-TROPSCH

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A Hybrid optimization Algorithm with Bayesian Inference for Probabilistic Model Updating

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2015年第8期30卷 602-619页

作者： Sun, Hao Betti, Raimondo Columbia Univ Dept Civil Engn & Engn Mech New York NY 10027 USA

A hybrid optimization methodology is presented for the probabilistic finite element model updating of structural systems. The model updating process is formulated as an inverse problem, analyzed by Bayesian inference, and solved using a hybrid optimization algorithm. The proposed hybrid approach is a combination of a modified artificial bee colony (MABC) algorithm and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The MABC includes four modifications compared to the standard ABC algorithm, which basically improve the global convergence of ABC in the solution phases of initialization, updating, selection, and rebirth. The BFGS is inserted to improve the finer solution search ability aiming at a higher solution accuracy. In brief, a probabilistic framework based on Bayesian inference is first derived so to get a regularized objective function for optimization. Then the proposed MABC-BFGS algorithm is applied to determine the unknown system parameters by minimizing the newly defined objective function. System parameters as well as the prediction error covariance are updated iteratively in the optimization process. Posterior distributions of the identified system parameters are determined using a weighted sum of Gaussian distributions. Finally, the effectiveness of the proposed approach is illustrated by the numerical data sets of the Phase I IASC-ASCE benchmark model and the experimental data sets of a three-storey frame structure (from the Los Alamos National Laboratory (LANL), New Mexico, United States).

关键词： Inverse method Statistical Data Model updating system parameter Gaussian distribution Probabilistic Model Los Alamos National Laboratory optimization Procedure optimization algorithms Bayesian inference weight sums Objective function activity based costing global convergence

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An automated optimization procedure for geometry parameters calibration of two-curvature conical heat source model

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2024年 197卷

作者： Jiao, Haihan Jin, Hui Southeast Univ Sch Civil Engn Jiangsu Key Lab Engn Mech Nanjing 211189 Peoples R China

Welding is an important joining method in industrial manufacturing, and optimization techniques combined with advanced simulation methods in welding have greatly promoted the development of industries. Modeling the heat transfer phenomenon during the welding process can provide a basis for reliably calculating the temperature and residual stress fields of the weldments. Therefore, accurately describing the geometric parameters of the welding heat source model is crucial. An interactive computational procedure is proposed to achieve efficient automatic parameter calibration, which is combined numerical simulation with optimization algorithm. This procedure defines a novel objective function called the total fusion zone error, which measures the disparity in fusion zone shape. It calculates the fusion zone difference for each element near the weld line between the simulated value and the test value, and sums them up. The particle swarm algorithm (PSO) is then selected for the optimization procedure, with some improvement of continuously compressing the particle's search domain and replacing its un-computed fitness value by the neighboring particle's results to reduce the iterative computation time. Synthetically, the proposed procedure is implemented in ABAQUS software to automatically calculate the objective function during optimization iterations. In a case study, the two-curvature conical heat source model is deduced, its calibrated parameters conclude temperature field results with valuable accuracy and efficiency, through this proposed multi-software cooperation automated optimization process. The method proposed in this paper can achieve the requirement of accurate programmatic solution of heat source model parameters, reducing the dependence of simulation efficiency and accuracy on researchers' experience.

关键词： Heat source model Geometry parameters Numerical simulation optimization algorithms Isoparametric transformation

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An efficient technique to solve combined economic and emission dispatch problem using modified Ant colony optimization

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2013年第4期38卷 545-556页

作者： Gopalakrishnan, R. Krishnan, A. KS Rangasamy Coll Technol Dept Elect & Elect Engn Tiruchengode 637215 India

Economic load dispatch is one of the vital purposes in electrical power system operation, management and planning. Economic dispatch problem is one of the most important problems in electric power system operation. In large scale system, the problem is more complex and difficult to find out optimal solution because it is nonlinear function and it contains number of local optimal. Combined economic emission dispatch (CEED) problem is to schedule the committed generating units outputs to meet the required load demand at minimum operating cost with minimum emission simultaneously. The main aim of economic load dispatch is to reduce the total production cost of the generating system and at the same time the necessary equality and inequality constraints should also be fulfilled. This leads to the development of CEED techniques. There are various techniques proposed by several researchers to solve CEED problem based on optimization techniques. But still some problems such as slower convergence and higher computational complexity exist in using the optimization techniques such as GA for solving CEED problem. This paper proposes an efficient and reliable technique for combined fuel cost economic optimization and emission dispatch using the Modified Ant Colony optimization algorithm (MACO) to produce better optimal solution. The simulation results reveal the significant performance of the proposed MACO approach.

关键词： Combined economic emission dispatch (CEED) optimization algorithms power demand Ant colony optimization modified Ant colony optimization (MACO)

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Social optimization and Pricing Policy in Cognitive Radio Networks with an Energy Saving Strategy

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MOBILE INFORMATION SYSTEMS 2016年第Pt.3期2016卷 2426580.1-2426580.1页

作者： Jin, Shunfu Chen, Si Zhang, Jinliang Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China Yanshan Univ Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066004 Peoples R China Minist Culture China Natl Ctr Publ Cultural Serv Beijing 110000 Peoples R China

The rapid growth of wireless application results in an increase in demand for spectrum resource and communication energy. In this paper, we firstly introduce a novel energy saving strategy in cognitive radio networks (CRNs) and then propose an appropriate pricing policy for secondary user (SU) packets. We analyze the behavior of data packets in a discrete-time single-server priority queue under multiple-vacation discipline. With the help of a Quasi-Birth-Death (QBD) process model, we obtain the joint distribution for the number of SU packets and the state of base station (BS) via the Matrix-Geometric Solution method. We assess the average latency of SU packets and the energy saving ratio of system. According to a natural reward-cost structure, we study the individually optimal behavior and the socially optimal behavior of the energy saving strategy and use an optimization algorithm based on standard particle swarm optimization (SPSO) method to search the socially optimal arrival rate of SU packets. By comparing the individually optimal behavior and the socially optimal behavior, we impose an appropriate admission fee to SU packets. Finally, we present numerical results to show the impacts of system parameters on the system performance and the pricing policy.

关键词： partition coefficients System performance energy saving fare policy Packet Cognitive radio data packets optimization algorithms Optimal Energy conservation arrival rate Radio networks

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