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A Comparison of Chaotic Electromagnetic Field optimization algorithms

International Journal Bioautomation 2024年第4期28卷 245-265页

作者： Roeva, Olympia Zoteva, Dafina Department of Bioinformatics and Mathematical Modelling Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Acad. G. Bonchev Str. bl. 105 Sofia1113 Bulgaria Department of Computer Informatics Faculty of Mathematics and Informatics Sofia University "St. Kliment Ohridski" 5 James Bourchier Blvd. Sofia1164 Bulgaria

This paper investigates the performance of various Electromagnetic Field optimization (EFO) algorithms. Chaos maps are proposed to improve the performance of EFO algorithms. Ten chaotic maps are incorporated in EFO – Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal and Tent. To compare the performance of the constructed EFO algorithms, a case study of the identification of the model parameters of a cultivation process model is studied. An experimental data set from E. coli BL21(DE3)pPhyt109 fed-batch cultivation process is used. Based on the results of 30 runs of each EFO, some statistical and InterCriteria analyzes are performed. As a result, the best performing EFO algorithms are iterative EFO and tent chaotic map EFO. These algorithms gave the best objective value (best and mean value) and had a good distribution of results. © 2024 by the authors.

关键词： optimization algorithms

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Convergence detection for optimization algorithms: Approximate-KKT stopping criterion when Lagrange multipliers are not available

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OPERATIONS RESEARCH LETTERS 2015年第5期43卷 484-488页

作者： Haeser, Gabriel de Melo, Vinicius V. Univ Sao Paulo Inst Math & Stat Sao Paulo SP Brazil Univ Fed Sao Paulo Inst Sci & Technol Sao Jose Dos Campos SP Brazil

In this paper we investigate how to efficiently apply Approximate-Karush-Kuhn-Tucker proximity measures as stopping criteria for optimization algorithms that do not generate approximations to Lagrange multipliers. We prove that the KKT error measurement tends to zero when approaching a solution and we develop a simple model to compute the KKT error measure requiting only the solution of a non-negative linear least squares problem. Our numerical experiments on a Genetic Algorithm show the efficiency of the strategy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Optimality conditions optimization algorithms Stopping criteria Genetic algorithms

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Derivative-Free optimization algorithms Based on Non-Commutative Maps

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IEEE CONTROL SYSTEMS LETTERS 2018年第4期2卷 743-748页

作者： Feiling, Jan Zeller, Amelie Ebenbauer, Christian Univ Stuttgart Inst Syst Theory & Automat Control D-70569 Stuttgart Germany Dr Ing HcF Porsche AG D-70435 Stuttgart Germany

A novel class of derivative-free optimization algorithms is developed. The main idea is to utilize certain non-commutative maps in order to approximate the gradient of the objective function. Convergence properties of... 详细信息

关键词： optimization algorithms adaptive control

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Design and development of vibratory cultivator using optimization algorithms

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SN APPLIED SCIENCES 2019年第10期1卷 1287页

作者： Rao, N. R. N. V. Gowripathi Chaudhary, Himanshu Sharma, A. K. Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur Rajasthan India MPUAT Udaipur Dept Farm Machinery & Power Engn Coll Technol & Engn Udaipur Rajasthan India

Tillage system design is one of the important areas of interest for farming community. Oscillatory tillage is one such area which reduces the draft consumption and plays a crucial role to farmers during soil manipulation process. The paper deals to design a vibratory mechanism to provide a continuous motion to the tillage tool for following a particular path adopted from the literature through proper synthesis theory and procedure. A four bar mechanism is designed through proper synthesis procedure to identify the dimensions. Analytical and optimal synthesis method is followed during the design process. optimization algorithms such as hybrid teaching-learning particle swarm optimization based algorithm (HTLPSO), teaching-learning based algorithm, and particle swarm optimization is used to find the values of the design variables. MATLAB is the software used for the synthesis and analysis process. It is observed in the study that designed four mechanism follows the required path for vibratory tillage operation. The results attained through optimization algorithm in HTLPSO performed better for the required path than other nature-inspired algorithms. Also the developed vibratory cultivator performed better in the field trials.

关键词： Soil Vibratory tillage Four bar mechanism optimization algorithms

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Improving cardiomyocyte model fidelity and utility via dynamic electrophysiology protocols and optimization algorithms

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JOURNAL OF PHYSIOLOGY-LONDON 2016年第9期594卷 2525-2536页

作者： Krogh-Madsen, Trine Sobie, Eric A. Christini, David J. Weill Cornell Med Coll Greenberg Div Cardiol 520 East 70th StStarr 463 New York NY 10021 USA Weill Cornell Med Coll Inst Computat Biomed 520 East 70th StStarr 463 New York NY 10021 USA Mt Sinai Sch Med Pharmacol & Syst Therapeut New York NY USA Weill Cornell Med Coll Dept Physiol & Biophys 520 East 70th StStarr 463 New York NY 10021 USA

Mathematical models of cardiac electrophysiology are instrumental in determining mechanisms of cardiac arrhythmias. However, the foundation of a realistic multiscale heart model is only as strong as the underlying cell model. While there have been myriad advances in the improvement of cellular-level models, the identification of model parameters, such as ion channel conductances and rate constants, remains a challenging problem. The primary limitations to this process include: (1) such parameters are usually estimated from data recorded using standard electrophysiology voltage-clamp protocols that have not been developed with model building in mind, and (2) model parameters are typically tuned manually to subjectively match a desired output. Over the last decade, methods aimed at overcoming these disadvantages have emerged. These approaches include the use of optimization or fitting tools for parameter estimation and incorporating more extensive data for output matching. Here, we review recent advances in parameter estimation for cardiomyocyte models, focusing on the use of more complex electrophysiology protocols and global search heuristics. We also discuss future applications of such parameter identification, including development of cell-specific and patient-specific mathematical models to investigate arrhythmia mechanisms and predict therapy strategies.

关键词： model parameters Electrophysiology Parameter identification desired output Parameter estimation Cardiac Arrhythmias Cardiac Electrophysiology optimization algorithms Cardiac Myocytes

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NEIGHBORHOOD SEARCH-BASED optimization algorithms FOR PRODUCTION SCHEDULING - A SURVEY

COMPUTER INTEGRATED MANUFACTURING SYSTEMS

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COMPUTER INTEGRATED MANUFACTURING SYSTEMS 1992年第2期5卷 167-176页

作者： BRANDIMARTE, P Dipartimento di Automatica e Informatica Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy

Neighbourhood search is one of the general strategies used in designing heuristic algorithms for discrete optimization. Apart from its simplicity from the conceptual and implementation point of view, a notable characteristic of neighbourhood search is its generality: no assumption is made about the objective and the constraints, whereas other heuristic methods depend on the particular problem at hand. Neighbourhood search is, to say the least, mathematically unexciting, and for many problems specific heuristic algorithms exist with better performance. However, from a practical point of view, the ease of conception and implementation of a neighbourhood search algorithm make it a most interesting candidate for the quick prototyping of optimization software for many domains, including manufacturing. These characteristics have justified the continuous interest in neighbourhood search. Some algorithms have been proposed to overcome the greatest shortcoming of neighbourhood search, i.e. the tendency to get stuck in a local minimum. In this paper the two most interesting neighbourhood search-based algorithms, simulated annealing and tabu search, are presented and evaluated by comparing them with an exact algorithm for a simple scheduling problem. Due to the complexity of optimization problems encountered in the CIM world, the practitioner will find these algorithms a most useful tool.

关键词： NEIGHBORHOOD SEARCH optimization algorithms COMPUTER-INTEGRATED MANUFACTURING

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Application of optimization algorithms for Solving Marine Seismic Survey Planning Problems with Bottom Stations in the Arctic Shelf

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IZVESTIYA-PHYSICS OF THE SOLID EARTH 2023年第6期59卷 1079-1088页

作者： Zaitsev, S. V. Tikhotskiy, S. A. Silaev, A. V. Anan'ev, A. A. Uzhegov, D. V. Kudryashov, I. Yu. Vasekin, B. V. Kondrashenko, S. I. Khlyupin, A. N. Kulygin, D. A. Bazilevich, S. O. Natl Res Univ Moscow Inst Phys & Technol Dolgoprudnyi 141700 Moscow oblast Russia MIPT OOO Ctr Engn & Technol Dolgoprudnyi 141700 Moscow Oblast Russia Russian Acad Sci Schmidt Inst Phys Earth Moscow 123995 Russia AO MAGE Moscow 121609 Russia

An algorithm for optimizing the trajectories and movement sequence of a fleet of marine seismic survey vessels in solving the problem of marine seismic surveys using bottom stations is presented. The algorithm is based on solving the traveling salesman problem with mixed deliveries and collections of goods (TSPDC). A description of the algorithm extension to a problem that takes into account static closed zones that simulate ice and meteorological conditions unsuitable for the ship movement is given. The Dubins path algorithm provides a path close to the minimum and takes into account real characteristics of the ship movement and its speed when performing various types of work (installing bottom stations, collecting stations, maneuvering, etc.). The scientific novelty of the study lies in applying the solution of the TSPDC to problems of marine geophysics with the condition of presence of closed zones and developing an algorithm for optimizing the work of seismic vessels with the use of bottom stations, which is relevant in the conditions of the Arctic shelf during the period of limited navigation. The algorithm described in the article makes it possible to take into account the return of the vessel for collecting the equipment when working with bottom stations in the transition zone. The developed algorithm for planning marine seismic surveys formed the basis of the application software. The formalization of the problem, the results of the algorithm operation, and examples of planning on test data are presented. The possible limitations for the proposed algorithm are raised. The obtained results are applicable for further use in the implementation of tasks on optimizing the work plan for marine seismic surveys with several vessels, both when planning seismic surveys and when adjusting plans directly on the ship. The use is also justified if it is necessary to reenter the profile (for example, when reworking out a defective work area).

关键词： marine seismic survey seismic exploration planning transition zone optimum planning artificial intelligence optimization algorithms Dubins path traveling salesman problem

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optimization algorithms for Power Grid Planning and Operational Problems.

Optimization Algorithms for Power Grid Planning and Operatio...

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作者： Schumacher, Kathryn M. University of Michigan

The modern electrical grid is an engineering marvel. The power grid is an incredibly complex system that largely functions very reliably. However, aging infrastructure and changing power consumption and generation trends will necessitate that new investments be made and new operational regimes be explored to maintain this level of reliability. One of the primary difficulties in power grid planning is the presence of uncertainty. In this thesis, we address short-term (i.e., day-ahead) and long-term power system planning problems where there is uncertainty in the forecasted demand for power, future renewable generation levels, and/or possible component failures. We initially consider a network capacity design problem where there is uncertainty in the nodal supplies and demands. This robust single-commodity network design problem underlies several applications including power transmission networks. Minimum cost capacity expansion decisions are made to ensure that there exists a feasible network flow solution for alpha% of the demand scenarios in the given set, where alpha is a parameter specified by the user. We next consider a day-ahead planning problem that is specifically applicable to the power grid. We present an extension of the traditional unit commitment problem where we additionally consider (1) a more stringent security requirement and (2) a more flexible set of recovery actions. We require that feasible operation is possible for any simultaneous failure of k generators and/or transmission lines (i.e., N-k security), and transmission switching may be used to recover from a failure event. Finally, we consider a transmission expansion planning problem where there is uncertainty in future loads, renewal generation outputs and line failures, and transmission switching is also allowed as a recovery action. We propose a robust optimization model where feasible operation is required for all loads and renewable generation levels within given ranges, and for all sing

关键词： optimization algorithms Power Grid Planning Under Uncertainty Two-stage Robust optimization Thesis

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Optimal Sizing and Placement of Capacitors in Radial Distribution Systems Based on Grey Wolf, Dragonfly and Moth-Flame optimization algorithms

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2019年第1期43卷 77-96页

作者： Diab, Ahmed A. Zaki Rezk, Hegazy Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya Egypt Natl Res Univ MPEI Elect Power Syst Dept Moscow Russia

The applications of grey wolf (GWO), dragonfly (DFO) and moth-flame (MFA) optimization techniques for optimum sitting of capacitors in various radial distribution systems (RDSs) are presented. The loss sensitivity factor is applied to determine the most candidate buses. Then, each optimization technique is utilized to find optimum placements and sizes of capacitors for determined Buses. In this study, 33-, 69- and 118-bus RDSs are considered for validating the effectiveness and efficiency of studied algorithms. The convergence performance is evaluated for tested RDSs using MATLAB/Simulink software. The obtained results confirm that GWO, DFO and MFA offer accurate convergence to the global minimum point of the objective function with high convergence speed. The ability of the studied techniques for enhancing voltage profiles with considered distribution systems is achieved. Finally, a comparison study between each studied technique with each other and with other techniques like PSO, fuzzy-GA, heuristic, DSA, TLBO, DA-PS, FPA and CSA has been carried out. The parameters of the comparison include: efficiency, execution time, the speed of convergence, minimizing total cost and increasing net savings. The results of comparison indicated that GWO-based algorithm has accurate convergence to optimal location and size of capacitor banks. In addition, it has the best performance in comparison with other techniques.

关键词： Radial distribution system Capacitor allocation Loss sensitivity factor optimization algorithms

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A Universal Decomposition for Distributed optimization algorithms

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 3044-3049页

作者： Van Scoy, Bryan Lessard, Laurent Miami Univ Dept Elect & Comp Engn Oxford OH 45056 USA Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA

In the distributed optimization problem for a multi-agent system, each agent knows a local function and must find a minimizer of the sum of all agents' local functions by performing a combination of local gradient evaluations and communicating information with neighboring agents. We prove that every distributed optimization algorithm can be factored into a centralized optimization method and a second-order consensus estimator, effectively separating the "optimization" and "consensus" tasks. We illustrate this fact by providing the decomposition for many recently proposed distributed optimization algorithms. Conversely, we prove that any optimization method that converges in the centralized setting can be combined with any second-order consensus estimator to form a distributed optimization algorithm that converges in the multi-agent setting. Finally, we describe how our decomposition may lead to a more systematic algorithm design methodology.

关键词： Optimized production technology Transfer functions Laplace equations Linear systems Linear programming Symbols Communication networks Large-scale systems network analysis and control optimization algorithms

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