Parameter identification is an important part of tire model development. The prediction performance of a tire model highly depends on the identified parameter values of the tire model. Different optimization algorithm...
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Parameter identification is an important part of tire model development. The prediction performance of a tire model highly depends on the identified parameter values of the tire model. Different optimization algorithms may yield different tire parameters with different computational accuracy. It is essential to find out which optimization algorithm is most likely to generate a set of parameters with the best prediction performance. In this study, four different MATLAB (R) optimization algorithms, including fminsearchcon, patternsearch, genetic algorithm (GA), and particles warm, are used to identify the parameters of a newly proposed in-plane flexible ring tire model. The reference data used for parameter identification are obtained through a ADAMS FTire (R) virtual cleat test. After parameters are identified based on above four algorithms, their performances are compared in terms of effectiveness, efficiency, reliability, and robustness. Once the best optimization algorithm for the proposed tire model is determined, this optimization algorithm is used to test different types of cost functions to determine which cost function is the best choice for tire model parameter identification. The study in this article provides some important insights for the tire model parameter identification.
A systematic approach for prioritization of protected areas is the use of artificial intelligence. This approach employs computer algorithms based on an objective function to identify the best network of areas to be p...
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A systematic approach for prioritization of protected areas is the use of artificial intelligence. This approach employs computer algorithms based on an objective function to identify the best network of areas to be protected. Site selection algorithms are commonly used to identify areas of high conservation value. This study used three types of heuristic algorithms (simulated annealing, greedy, rarity) to prioritize areas for protection in Mazandaran Province of Iran using Marxan software. The goal was to select a conservation network with the smallest possible area in which maximum protection targets are achievable. The effects of spatial scale, algorithm, and zone compactness were also examined. We found that the existing network of protected areas is inadequate to achieve conservation targets. The algorithm results provided the best areas for supplementation of the current network. The simulated annealing algorithm provided the most plausible results for all scenarios. These results can be used to modify the existing boundaries of the protected areas network and introduce new sites for protection of plant and animal species. (C) 2014 Elsevier GmbH. All rights reserved.
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...
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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 the novel algorithms are established and simulation examples are presented.
The so-called Fourth Paradigm has witnessed a boom during the past two decades, with large volumes of observational data becoming available to scientists and engineers. Big data is characterized by the rule of the fiv...
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The so-called Fourth Paradigm has witnessed a boom during the past two decades, with large volumes of observational data becoming available to scientists and engineers. Big data is characterized by the rule of the five Vs: Volume, Variety, Value, Velocity and Veracity. The concept of big data naturally matches well with the features of geoengineering and geoscience. Large-scale, comprehensive, multidirectional and multifield geotechnical data analysis is becoming a trend. On the other hand, Machine learning (ML), Deep Learning (DL) and optimization Algorithm (OA) provide the ability to learn from data and deliver in-depth insight into geotechnical problems. Researchers use different ML, DL and OA models to solve various problems associated with geoengineering and geoscience. Consequently, there is a need to extend its research with big data research through integrating the use of ML, DL and OA techniques. This work focuses on a systematic review on the state-of-the-art application of ML, DL and OA algo-rithms in geoengineering and geoscience. Various ML, DL, and OA approaches are firstly concisely intro-duced, concerning mainly the supervised learning, unsupervised learning, deep learning and optimization algorithms. Then their representative applications in the geoengineering and geoscience are summarized via VOSviewer demonstration. The authors also provided their own thoughts learnt from these applica-tions as well as work ongoing and future recommendations. This review paper aims to make a compre-hensive summary and provide fundamental guidelines for researchers and engineers in the discipline of geoengineering and geoscience or similar research areas on how to integrate and apply ML, DL and OA methods.(c) 2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
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 ...
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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.
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 charact...
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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.
Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning. A new two-dimensional storm identification method simultaneously seeking in two directions is propo...
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Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning. A new two-dimensional storm identification method simultaneously seeking in two directions is proposed, and identification results are used to discuss storm tracking algorithms. Three modern optimization algorithms （simulated annealing algorithm, genetic algorithm and ant colony algorithm） are tested to match storms in successive time intervals. Preliminary results indicate that the simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters, whereas the genetic algorithm is unsatisfaetorily constrained by the mode of genetic operations Experiments provide not only the feasibility and characteristics of storm tracking with modern optimization algorithms, but also references for studies and applications in relevant fields.
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 manipulat...
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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.
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 fac...
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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.
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 base...
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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).
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