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New method for determining search directions for interior-point algorithms in linear optimization

OPTIMIZATION LETTERS 2018年第5期12卷 1099-1116页

作者： Darvay, Zsolt Takacs, Petra-Renata Babes Bolyai Univ Fac Math & Comp Sci 1 Mihail Kogalniceanu St Cluj Napoca 400084 Romania

We introduce an interior-point algorithm for linear optimization, which is based on a new technique for determining search directions. This method consists of a new type of transformation on the centering equations of the system which characterizes the central path. It can be shown that these new directions cannot be derived from usual kernel functions. Therefore, we extend the concept of the kernel functions, and we establish an equivalence between this approach and the proposed method for obtaining search directions. Moreover, we prove the polynomial complexity of the algorithm.

关键词： interior-point algorithm Linear optimization Algebraic equivalent transformation Search direction Newton's method Polynomial complexity

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Novel design of Morlet wavelet neural network for solving second order Lane-Emden equation

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MATHEMATICS AND COMPUTERS IN SIMULATION 2020年第0期172卷 1-14页

作者： Sabir, Zulqurnain Wahab, Hafiz Abdul Umar, Muhammad Sakar, Mehmet Giyas Raja, Muhammad Asif Zahoor Hazara Univ Dept Math & Stat Mansehra Pakistan Yuzuncu Yil Univ Fac Sci Dept Math Van Turkey COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan

In this study, a novel computational paradigm based on Morlet wavelet neural network (MWNN) optimized with integrated strength of genetic algorithm (GAs) and interior-point algorithm (IPA) is presented for solving second order Lane-Emden equation (LEE). The solution of the LEE is performed by using modelling of the system with MWNNs aided with a hybrid combination of global search of GAs and an efficient local search of IPA. Three variants of the LEE have been numerically evaluated and their comparison with exact solutions demonstrates the correctness of the presented methodology. The statistical analyses are performed to establish the accuracy and convergence via the Theil's inequality coefficient, mean absolute deviation, and Nash Sutcliffe efficiency based metrics. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Lane-Emden equation Artificial neural networks Singular Genetic algorithm Nonlinear interior-point algorithm

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Analysis of Oscillatory Behavior of Heart by Using a Novel Neuroevolutionary Approach

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IEEE ACCESS 2020年 8卷 86674-86695页

作者： Khan, Adnan Sulaiman, Muhammad Alhakami, Hosam Alhindi, Ahmad Abdul Wali Khan Univ Dept Math Mardan 23200 Pakistan Umm Al Qura Univ Dept Comp Sci Mecca 21955 Saudi Arabia Umm Al Qura Univ Ctr Innovat & Dev AI CIADA Mecca 21955 Saudi Arabia

This paper aims at the analysis of the VdP heartbeat mathematical model. We have analysed the conditionality of a mathematical model which represents the oscillatory behaviour of the heart. A novel neuroevolutionary approach is chosen to analyse the mathematical model. The characteristics of the cardiac pulse of the heart are examined by considering two major scenarios with sixteen different cases. Artificial neural networks (ANNs) are constructed to obtain the best solutions for the heartbeat model. Unknown weights are finely tuned by a combination of a global search technique the Harris Hawks Optimizer (HHO) and a local search technique the interior point algorithm (IPA). Stable behaviour of solutions obtained by considering different cases demonstrates that the model under consideration is well-conditioned. The accuracy of our novel procedure is established by getting the lowest residual errors in our solution for all cases. Graphical and statistical analysis are added to further elaborate the accuracy of our approach.

关键词： Cardiac pulse model hybridized soft computing artificial neural networks non-linear ordinary differential equations heuristics interior-point algorithm Harris Hawks optimizer

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Design of nature-inspired heuristic paradigm for systems in nonlinear electrical circuits

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NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 7121-7137页

作者： Mehmood, Ammara Zameer, Aneela Aslam, Muhammad Saeed Raja, Muhammad Asif Zahoor Pakistan Inst Engn & Appl Sci Dept Elect Engn Islamabad Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan Univ Adelaide Sch Elect & Elect Engn Adelaide SA Australia COMSATS Inst Informat Technol Dept Elect Engn Attock Campus Attock Pakistan

In the present study, a novel application of nature-inspired heuristics is presented for problems in nonlinear circuit analysis using neural networks, particle swarm optimization (PSO), and interior-point algorithm (IPA) as well as integrated approach PSO-IPA. The governing system models of resistor-capacitor circuits with nonlinear capacitance as well as resistor-inductor circuits with nonlinear inductance are mathematically modeled through competency of neural networks and weights of these networks are trained for global search with PSO hybrid with IPA for speedy refinements. The designed technique is applied on a number of scenarios by taking different values of resistance, current, voltage inductance, and capacitance parameters in nonlinear electrical circuit models. Comparative study with Adams numerical solvers having matching of the order 10(-04)-10(-07) and consistently attaining near-optimal gauges of performance indices based on root-mean-squared error, Theil's inequality coefficient, and Nash-Sutcliffe efficiency metrics validate and verify the efficacy of the scheme.

关键词： Electronic circuits Artificial neural networks Particle swarm optimization interior-point algorithm Hybrid computing

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A Full-Newton Step Feasible IPM for Semidefinite Optimization Based on a Kernel Function with Linear Growth Term

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Wuhan University Journal of Natural Sciences 2020年第6期25卷 501-509页

作者： GENG Jie ZHANG Mingwang PANG Jinjuan Mathematics Department Anhui Institute of Information TechnologyWuhu 241000AnhuiChina College of Science China Three Gorges UniversityYichang 443002HubeiChina

In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth *** kernel function is used both for determining the search directions and for measuring the distance between the given iterate and theμ-center for the *** developing a new norm-based proximity measure and some technical results,we derive the iteration bound that coincides with the currently best known iteration bound for the algorithm with small-update *** our knowledge,this result is the first instance of full-Newton step feasible interior-point method for SDO which involving the kernel function.

关键词： semidefinite optimization interior-point algorithm kernel function iteration complexity

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Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind

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APPLIED SOFT COMPUTING 2021年 104卷 107193-107193页

作者： Khan, Babar Sattar Raja, Muhammad Asif Zahoor Qamar, Affaq Chaudhary, Naveed Ishtiaq Univ Engn & Technol Peshawar Dept Elect Energy Syst Engn USPCAS E Peshawar Khyber Pakhtunk Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Campus Attock Attock Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Int Islamic Univ Dept Elect Engn Islamabad Pakistan

In this investigation, nature-inspired heuristic strategy exploiting moth flame optimization (MFO) algorithm combined with active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) are presented to take care of the enhancement issues of economic load dispatch (ELD) problem involving valve point loading effect (VPLE) and stochastic wind (SW). The strength of MFO algorithm is used as a global search mechanism that explore and exploit the entire search space while ASA, IPA and SQP are responsible for refinement of local optimum. The performance of the design system is based on 40 generating units including 37 thermal and 3 wind power units and is evaluated to verify the effectiveness of the scheme. The worth of the design integrated heuristic of MFO algorithm is endorsed through outcomes of the state of the art counterpart solvers in case of ELD problems integrated with wind power units in terms of cost minimization and computational complexity parameters. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Moth flame optimization algorithm Sequential quadratic programming Active-set algorithm interior-point algorithm Economic load dispatch Integrated power systems

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Energy scheduling model considering penalty mechanism in transactive energy markets: A hybrid approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 129卷 106742-106742页

作者： Dulipala, J. K. S. S. Balaji Debbarma, Sanjoy Natl Inst Technol Meghalaya Dept Elect Engn Shillong Meghalaya India

Massive penetration of distributed energy resources (DER) in distribution networks necessitates the need for a dynamic marketplace in the bottom layer to balance the generation and load demand. In future, transactive energy (TE) concept would establish such a dynamic platform that engages the local players in energy transactions. However, uncertainties associated with the local entities and the submission of the unrealistic bids by the agents would significantly upset the energy trading in the TE market. As a result, the DER agents would encounter unplanned penalties for the unsupplied energy, thereby discouraging them to participate in the energy markets. Moreover, treating DER agents equally as the traditional generators when enforcing penalties is also not encouraging. To this context, a well-organized decision-making model for DER agents is proposed in this paper that has the ability to select most profitable actions among the multiple bids while turning down the effects of penalties. The proposed model manages the DER units and performs transactions optimally such that the overall profit increases. Decisive operation is performed using a hybrid approach that comprises of water cycle algorithm (WCA) and interior point (IP) model and termed as WCIP algorithm (WCIPA). The performance of hybrid WCIPA is validated by comparing it with the state-of-art algorithms for the given problem.

关键词： Transactive energy Penalty mechanisms Water cycle algorithm interior-point algorithm WCIPA Local market

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Learning to steer nonlinear interior-point methods

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2019年第4期7卷 381-419页

作者： Kuhlmann, Renke Ctr Ind Math ZeTeM Optimizat & Optimal Control Bibliothekstr 5 D-28359 Bremen Germany

interior-point or barrier methods handle nonlinear programs by sequentially solving barrier subprograms with a decreasing sequence of barrier parameters. The specific barrier update rule strongly influences the theoretical convergence properties as well as the practical efficiency. While many global and local convergence analyses consider a monotone update that decreases the barrier parameter for every approximately solved subprogram, computational studies show a superior performance of more adaptive strategies. In this paper we interpret the adaptive barrier update as a reinforcement learning task. A deep Q-learning agent is trained by both imitation and random action selection. Numerical results based on an implementation within the nonlinear programming solver WORHP show that the agent successfully learns to steer the barrier parameter and additionally improves WORHP's performance on the CUTEst test set.

关键词： Nonlinear programming Constrained optimization interior-point algorithm Reinforcement learning Deep Q-learning

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Machine Learning Based Device Simulation Using Multi-variable Non-linear Regression to Assess the Impact of Device Parameter Variability on Threshold Voltage of Double Gate-All-Around (DGAA) MOSFET 2

Machine Learning Based Device Simulation Using Multi-variabl...

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IEEE 2nd International Conference on Circuits and Systems (ICCS)

作者： Moparthi, Sandeep Yadav, Chandan Saramekala, Gopi Krishna Tiwari, Pramod Kumar NIT Calicut Dept ECE Kozhikode 673601 Kerala India IIT Patna Dept EE Patna 801103 Bihar India

ISBN: (纸本)9781728191225

For the first time, the machine learning approach is proposed for the analysis of device parameter variability impact on the threshold voltage of silicon-nanotube-based double gate-all-around (DGAA) MOSFET using multi-variable non-linear regression with five input variables. interior-point algorithm is implemented in MATLAB and used for training the hypothesis. algorithm is supplied with 2000 random initial guesses to ensure global minima in optimization for healthier accuracy at comfortable simulation time. The worst-case accuracy of 96.33 percent is achieved in prediction with an average percentage prediction error of 0.83 percent. Study revealed that, with proper training and optimization of the hypothesis (fitness) function it is possible to predict the threshold voltage of the device by keeping good trade-off between computation time and accuracy.

关键词： silicon nanotube threshold voltage non-linear regression hypothesis interior-point algorithm

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An Optimization Approach Based on the interior-point Methodology for the Tertiary Control of Modernized Microgrids

An Optimization Approach Based on the Interior-Point Methodo...

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IEEE SoutheastCon Conference

作者： Woodruff, Isaiah D. Davari, Masoud Georgia Southern Univ Dept Elect & Comp Engn Statesboro Campus Statesboro GA 30458 USA

ISBN: (纸本)9781728101378

With the rise in popularity of the modernized microgrids (MMGs), the addition of a controller to maximize economic efficiency while taking into account environmental impact is crucial. Tertiary control is at the highest control level, considering economic concerns related to the optimal operation of the microgrid and using a sampling time from minutes to hours;tertiary controls manage the flow of power between the microgrid and the connected grid. In MMGs' tertiary controls, the use of optimization algorithms to minimize an objective function with equality and inequality constraints allows for powerful control over the system. In this paper, the interior-point algorithm is proposed and utilized in order to minimize the required objective. The suggested method is robust against weather conditions and accommodates varying demands in both ac and de grids. To perform optimization and simulations, MATLAB outputs a lookup table, which can be easily implemented into any system. This is a 2-dimensional array with ac load requirements on the rows and dc load requirements on the columns. Furthermore, due to load capabilities, where the resolution of the system gets too fine, simple algebra can till in the missing information. Visualization of these results is key to understand what MMG's devices would output for a certain load.

关键词： interior-point algorithm island mode of operation modernized microgrids optimization tertiary controls

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