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Capping methods for the automatic configuration of optimization algorithms

COMPUTERS & OPERATIONS RESEARCH 2022年 139卷 1页

作者： de Souza, Marcelo Ritt, Marcus Lopez-Ibanez, Manuel Santa Catarina State Univ Dept Software Engn Florianopolis SC Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Malaga Sch Comp Sci Malaga Spain Univ Manchester Alliance Manchester Business Sch Manchester Lancs England

Automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. Configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit.

关键词： Automatic algorithm configuration Capping methods optimization algorithms Parameter tuning

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Using optimization algorithms-Based ANN to Determine the Temperatures in Timber Exposed to Fire for a Long Duration

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BUILDINGS 2022年第12期12卷 2265页

作者： Nikoo, Mehdi Hafeez, Ghazanfarah Cachim, Paulo Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada Univ Aveiro Dept Civil Engn RISCO P-3810193 Aveiro Portugal

The article investigates the temperature prediction in rectangular timber cross-sections exposed to fire. Timber density, exposure time, and the point coordinates within the cross-section are treated as inputs to determine the temperatures. A total of 54,776 samples of wood cross-sections with a variety of characteristics were considered in this study. Of the sample data, 70% was dedicated to training the networks, while the remaining 30% was used for testing the networks. Feed-forward networks with various topologies were employed to examine the temperatures of timber exposed to fire for more than 1500 s. The weight of the artificial neural network was optimized using bat and genetic algorithms. The results conclude that both algorithms are efficient and accurate tools for determining the temperatures, with the bat algorithm being marginally superior in accuracy than the genetic algorithm.

关键词： temperatures timber fire artificial neural network optimization algorithms

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A Comparative Study of Genetic and Particle Swarm optimization algorithms and Their Hybrid Method in Water Flooding optimization

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2018年第10期140卷 102903-102903页

作者： Siavashi, Majid Yazdani, Mohsen Iran Univ Sci & Technol Appl Multiphase Fluid Dynam Lab Sch Mech Engn Tehran *** Iran Iran Univ Sci & Technol Sch Mech Engn Tehran *** Iran

optimization of oil production from petroleum reservoirs is an interesting and complex problem which can be done by optimal control of well parameters such as their flow rates and pressure. Different optimization techniques have been developed yet, and metaheuristic algorithms are commonly employed to enhance oil recovery projects. Among different metaheuristic techniques, the genetic algorithm (GA) and the particle swarm optimization (PSO) have received more attention in engineering problems. These methods require a population and many objective function calls to approach more the global optimal solution. However, for a water flooding project in a reservoir, each function call requires a long time reservoir simulation. Hence, it is necessary to reduce the number of required function evaluations to increase the rate of convergence of optimization techniques. In this study, performance of GA and PSO are compared with each other in an enhanced oil recovery (EOR) project, and Newton method is linked with PSO to improve its convergence speed. Furthermore, hybrid genetic algorithm-particle swarm optimization (GA-PSO) as the third optimization technique is introduced and all of these techniques are implemented to EOR in a water injection project with 13 decision variables. Results indicate that PSO with Newton method (NPSO) is remarkably faster than the standard PSO (SPSO). Also, the hybrid GA-PSO method is more capable of finding the optimal solution with respect to GA and PSO. In addition, GA-PSO, NPSO, and GANPSO methods are compared and, respectively, GA-NPSO and NPSO showed excellence over GA-PSO.

关键词： Reservoirs algorithms optimization Floods Particle swarm optimization Water Wells Flow (Dynamics) optimization algorithms Particulate matter

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A Systematic Review of optimization algorithms for Structural Health Monitoring and Optimal Sensor Placement

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SENSORS 2023年第6期23卷 3293-3293页

作者： Hassani, Sahar Dackermann, Ulrike Univ New South Wales Ctr Infrastruct Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia

In recent decades, structural health monitoring (SHM) has gained increased importance for ensuring the sustainability and serviceability of large and complex structures. To design an SHM system that delivers optimal monitoring outcomes, engineers must make decisions on numerous system specifications, including the sensor types, numbers, and placements, as well as data transfer, storage, and data analysis techniques. optimization algorithms are employed to optimize the system settings, such as the sensor configuration, that significantly impact the quality and information density of the captured data and, hence, the system performance. Optimal sensor placement (OSP) is defined as the placement of sensors that results in the least amount of monitoring cost while meeting predefined performance requirements. An optimization algorithm generally finds the "best available" values of an objective function, given a specific input (or domain). Various optimization algorithms, from random search to heuristic algorithms, have been developed by researchers for different SHM purposes, including OSP. This paper comprehensively reviews the most recent optimization algorithms for SHM and OSP. The article focuses on the following: (I) the definition of SHM and all its components, including sensor systems and damage detection methods, (II) the problem formulation of OSP and all current methods, (III) the introduction of optimization algorithms and their types, and (IV) how various existing optimization methodologies can be applied to SHM systems and OSP methods. Our comprehensive comparative review revealed that applying optimization algorithms in SHM systems, including their use for OSP, to derive an optimal solution, has become increasingly common and has resulted in the development of sophisticated methods tailored to SHM. This article also demonstrates that these sophisticated methods, using artificial intelligence (AI), are highly accurate and fast at solving complex problems.

关键词： optimization algorithms structural health monitoring optimal sensor placement

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Possibilities and Problems of Station-Level optimization algorithms Integration into the Application Software of Program and Technical Complexes of Intelligent Automated Control Systems of TPP

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IFAC-PapersOnLine 2021年第13期54卷 75-80页

作者： E.K. Arakelyan S.V. Mezin F.F. Pashchenko A.A. Kosoy ACSHP Department National Research University “MPEI” Moscow 111250 Russia V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow 117997 Russia

The possibility of integrating station-level optimization algorithms into the application software of modern program and technical complexes (PTC) in order to increase the intelligence of the automatic control systems (ACS TP) of thermal power plants is considered. On the example of the actual task of managing the current technical and economic indicators (TEI) of power units, general methodological approaches to the development of a set of algorithms for calculating and analyzing current and regulatory indicators are considered. The article presents a methodology for evaluating the feasibility and timing of restoration work on the cooling surfaces of the condenser in order to eliminate or minimize the deviation of the current increased pressure in it from the standard value. The complexities of integrating algorithms for calculating and analyzing block-level TEI into the PTC software are shown, and possible ways to solve them are given.

关键词： Integration optimization algorithms application software program technical complexes TEI calculation analysis general approaches station level possibility complexity solutions

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Considerations on optimal design of hybrid power generation systems using whale and sine cosine optimization algorithms

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Journal of Electrical Systems and Information Technology 2018年第3期5卷 312-325页

作者： Mostafa A. Algabalawy Almoataz Y. Abdelaziz Said F. Mekhamer Shady H.E. Abdel Aleem General Motors Egypt Electrical Power and Machines Faculty of Engineering Ain Shams University Egypt Mathematical Physical and Engineering Sciences 15th of May Higher Institute of Engineering Cairo Egypt

Nowadays, the continuous increase of power demand leads to various challenges for distribution system operators (DSOs) such as power quality, system stability and reliability. Microgrids (MGs) and hybrid power generation systems (HPGSs) can play a significant role in solving these issues while improving the performance of electrical power systems. In this paper, an optimal multi-criteria design of a grid-connected HPGS is introduced, taking into consideration involvement of a natural gas distribution network (NGDN) in the proposed configuration, where the NGDN supplies natural gas to a gas turbine. The HPGS system consists of wind turbines (WT), photovoltaic (PV) arrays, battery banks (BBs), gas turbines (GTs), in addition to a utility grid (UG). Two different meta-heuristic optimization algorithms, namely whale, and sine cosine, are employed to find the optimal design of the system for minimizing the total annual cost and system emissions. A detailed comparative study of the results with results of the cuckoo search and firefly optimization algorithms is presented to show the robustness of the used techniques.

关键词： Distributed generation Power generation systems Natural gas optimization algorithms

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Dynamic CMOS Load Balancing and Path Oriented in Time optimization algorithms to Minimize Delay Uncertainties from Process Variations

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VLSI DESIGN 2010年第unknown期2010卷 230783-1-230783-13页

作者： Yelamarthi, Kumar Chen, Chien-In Henry Cent Michigan Univ Sch Engn & Technol Mt Pleasant MI 48859 USA Wright State Univ Dept Elect Engn Dayton OH 45435 USA

The complexity of timing optimization of high-performance circuits has been increasing rapidly in proportion to the shrinking CMOS device size and rising magnitude of process variations. Addressing these significant challenges, this paper presents a timing optimization algorithm for CMOS dynamic logic and a Path Oriented IN Time (POINT) optimization flow for mixed-static-dynamic CMOS logic, where a design is partitioned into static and dynamic circuits. Implemented on a 64-b adder and International Symposium on Circuits and Systems (ISCAS) benchmark circuits, the POINT optimization algorithm has shown an average improvement in delay by 38% and delay uncertainty from process variations by 35% in comparison with a state-of-the-art commercial optimization tool.

关键词： Load balancing dynamic circuit process change Byways optimization algorithms Oriented delay

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Nature-Inspired optimization algorithms for Text Document Clustering-A Comprehensive Analysis

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algorithms 2020年第12期13卷 345-345页

作者： Abualigah, Laith Gandomi, Amir H. Elaziz, Mohamed Abd Hussien, Abdelazim G. Khasawneh, Ahmad M. Alshinwan, Mohammad Houssein, Essam H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Fayoum Univ Fac Sci Faiyum 63514 Egypt Minia Univ Fac Comp & Informat Al Minya 61519 Egypt

Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm optimization (PSO) Algorithm, Ant Colony optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.

关键词： nature-inspired optimization algorithms machine learning optimization problems text clustering applications

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Gradient Descent optimization algorithms for Decoding SCMA Signals

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2021年第1期20卷

作者： Vidal-Beltran, Sergio Bonilla, Jose Luis Lopez Pinon, Fernando Martinez Yalja-Montiel, Jesus Inst Politecn Nacl Escuela Super Ingn Mecan & Elect Zacatenco Ciudad De Mexico Mexico Inst Politecn Nacl Ctr Invest & Innovac Tecnol Azcapotzalco Ciudad De Mexico Mexico Inst Politecn Nacl Ctr Invest Comp Ciudad De Mexico Mexico

Recently, technologies based on neural networks (NNs) and deep learning have improved in different areas of Science such as wireless communications. This study demonstrates the applicability of NN-based receivers for detecting and decoding sparse code multiple access (SCMA) codewords. The simulation results reveal that the proposed receiver provides highly accurate predictions based on new data. Moreover, the performance analysis results of the primary optimization algorithms used in machine learning are presented in this study.

关键词： 5G neural networks NOMA optimization algorithms SCMA supervised learning

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A comparison of various optimization algorithms of protein-ligand docking programs by fitness accuracy

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JOURNAL OF MOLECULAR MODELING 2014年第7期20卷 2251-1-2251-10页

作者： Guo, Liyong Yan, Zhiqiang Zheng, Xiliang Hu, Liang Yang, Yongliang Wang, Jin Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Chinese Acad Sci Changchun Inst Appl Chem State Key Lab Electroanalyt Chem Changchun 130022 Jilin Peoples R China Dalian Univ Technol Sch Life Sci & Biotechnol Ctr Mol Med Dalian 116024 Peoples R China SUNY Stony Brook Dept Chem & Phys Stony Brook NY 11794 USA

In protein-ligand docking, an optimization algorithm is used to find the best binding pose of a ligand against a protein target. This algorithm plays a vital role in determining the docking accuracy. To evaluate the relative performance of different optimization algorithms and provide guidance for real applications, we performed a comparative study on six efficient optimization algorithms, containing two evolutionary algorithm (EA)-based optimizers (LGA, DockDE) and four particle swarm optimization (PSO)-based optimizers (SODock, varCPSO, varCPSO-ls, FIPSDock), which were implemented into the protein-ligand docking program AutoDock. We unified the objective functions by applying the same scoring function, and built a new fitness accuracy as the evaluation criterion that incorporates optimization accuracy, robustness, and efficiency. The varCPSO and varCPSO-ls algorithms show high efficiency with fast convergence speed. However, their accuracy is not optimal, as they cannot reach very low energies. SODock has the highest accuracy and robustness. In addition, SODock shows good performance in efficiency when optimizing drug-like ligands with less than ten rotatable bonds. FIPSDock shows excellent robustness and is close to SODock in accuracy and efficiency. In general, the four PSO-based algorithms show superior performance than the two EA-based algorithms, especially for highly flexible ligands. Our method can be regarded as a reference for the validation of new optimization algorithms in protein-ligand docking.

关键词： Protein-ligand docking AutoDock software optimization algorithms Evolutionary algorithms (EAs) Particle swarm optimizations (PSOs)

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