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Constraint control method of optimization and its application to design of steel frames

SCIENTIA IRANICA 2019年第4期26卷 2241-2257页

作者： Mansouri, S. F. Maheri, M. R. Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Shiraz Univ Dept Civil Engn Shiraz Iran

Different optimization methods are available for optimum design of structures including classical optimization techniques and metaheuristic optimization algorithms. However, engineers do not generally use optimization techniques to design a structure. They attempt to decrease the structural weight and increase its performance and efficiency, empirically, by changing the variables and controlling the constraints. Based on this professional engineering design philosophy, in this paper, a simple algorithm, termed the Constraint Control Method (CCM), is developed and presented whereby optimum design is achieved gradually by controlling the problem constraints. Starting with oversized sections, the design was gradually improved by changing sections based on a 'control function' and controlling the constraints to be below the target values. As the constraints moved towards their targets, the design moved towards an optimum. The general functionality of the proposed algorithm was first demonstrated by solving several linear and nonlinear mathematical problems, which had exact answers. The performance of the algorithm was then evaluated through comparing design optimization results of three 2D steel frame benchmark problems with those of other metaheuristic optimization solutions. The proposed method led to the minimum structural weight while performing a considerably small number of structural analyses compared to other optimization methods. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： Constraint control method Optimum design Steel frames metaheuristic optimization algorithms Global search

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A stigmergic approach to teaching-learning-based optimization for continuous domains

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 62卷

作者： Meghdadi, Aghdas Akbarzadeh-T, M. R. Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Dept Elect Engn Mashhad Razavi Khorasan Iran

Teaching-Learning-Based optimization (TLBO) has shown considerable success in solving complex optimization problems by imitating the higher cognitive leader-follower interactions among students and teachers in a classroom. Here, we propose to solve function optimization with a stigmergic probabilistic memory structure based on the indirect cooperation and distributed information exchange among students of a class along with a diversity preserving operator. The stigmergic approach integrates the historical experience, while the probabilistic representation and the diversity preserving operators avoid premature convergence. The resulting Stigmergic Real domain TLBO (SRTLBO) is applied to optimization problems with continuous variables. The proposed SRTLBO is evaluated on 40 standard benchmark functions, as well as in modeling the earthquake source and wave propagation in the East of Iran from actual data. Results indicate overall improved solutions of the SRTLBO algorithm when compared to several competing algorithms. In several cases, particularly those functions with a simpler structure, considerable improvement is also observed in the rate of convergence to the optimum solution. Superscript/Subscript Available

关键词： metaheuristic optimization algorithms Swarm intelligence Stigmergy Teaching-learning-based optimization Earthquake ground motion

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PID-/FOPID-based frequency control of zero-carbon multisources-based interconnected power systems underderegulated scenarios

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第2期31卷

作者： Ebrahim, Mohamed Ahmed Becherif, Mohamed Abdelaziz, Almoataz Y. Benha Univ Dept Elect Engn Fac Engn Shoubra Cairo Egypt Univ Bourgogne Franche Comte UTBM CNRS FEMTO STInst Belfort France Future Univ Egypt Fac Engn & Technol Cairo Egypt

Zero-carbon multienergy sources (ZCESs) integration into a power system becomes mandatory to reduce energy production costs and fuel-burning emissions. Although, the energy mix of ZCESs with high penetration levels arises at moderated frequency as well as voltage response deterioration of the interconnected power systems. These response deteriorations stem from the fluctuation nature of ZCESs. Therefore, the necessity for overcoming these performance degradations is not an option for power system planners and operators. Thanks to fractional order (FO) mathematics in conjunction with metaheuristic optimization algorithms (MOAs) as they can be employed to enhance the damping of ZCESs oscillations under energy mix uncertainties. Three-area solar thermal-wind-hydro (STWH) power system, including system's uncertainties such as data telemetry delays and governor dead bandsand generation rate constraints, is considered to accomplish this study. The performance of STWH system equipped with FOPID-based load frequency controllers tuned by various modern MOAs (FOPID-based LFCs) has been compared to the classical PID-LFC controllers governed by the same MOAs, to demonstrate the superior efficiency of the FOPID-based LFCs. The efficacy of FOPID-based LFCs and their effectiveness compared to the predecessors are verified during simulation results.

关键词： fractional order PID load frequency control metaheuristic optimization algorithms stability zero‐ carbon energy resources

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Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2020年第0期142卷 102790-000页

作者： Mortazavi, Ali Ege Univ Grad Sch Nat & Appl Sci Izmir Turkey

In contrast with conventional structural optimization benchmark problems real size structures mostly contain a large number of members and their optimal design provide a serious challenging area for optimization methods. In this regard, current study deals with assessing the search performance of the recently developed fuzzy reinforced metaheuristic technique so called Interactive Fuzzy Search Algorithm on the optimization of large-scale structures. This method is a self-adaptive and parameter-free algorithm which applies a dual-module fuzzy decision mechanism to adjust its search behavior during the optimization process. This mechanism employs two nine rule fuzzy modules which permanently monitor the agents updating process and based on the governing conditions of the problem emphasize their exploration or exploitation search behavior. Attained results show that proposed method can adopt itself with the extensive search space of the studied problems. Form both accuracy and stability aspects Interactive Fuzzy Search Algorithm provides promising results on solving large-scale structural optimization problems.

关键词： Large-scale structure problems metaheuristic optimization algorithms Fuzzy decision mechanism

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Sea Lion optimization Algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第5期10卷 388-395页

作者： Masadeh, Raja Mahafzah, Basel A. Sharieh, Ahmad World Islamic Sci & Educ Univ Comp Sci Dept Amman Jordan Univ Jordan Comp Sci Dept Amman Jordan

This paper suggests a new nature inspired metaheuristic optimization algorithm which is called Sea Lion optimization (SLnO) algorithm. The SLnO algorithm imitates the hunting behavior of sea lions in nature. Moreover, it is inspired by sea lions' whiskers that are used in order to detect the prey. SLnO algorithm is tested with 23 well-known test functions (Benchmarks). optimization results show that the SLnO algorithm is very competitive compared to Particle Swarm optimization (PSO), Whale optimization Algorithm (WOA), Grey Wolf optimization (GWO), Sine Cosine Algorithm (SCA) and Dragonfly Algorithm (DA).

关键词： optimization metaheuristic optimization algorithms Benchmarks Sea Lion optimization Algorithm (SLnO)

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Parameter estimation in single-phase transformers via the generalized normal distribution optimizer while considering voltage and current measurements

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RESULTS IN ENGINEERING 2024年 21卷

作者： Camelo-Daza, Juan David Betancourt-Alonso, Diego Noel Montoya, Oscar Danilo Gomez-Vargas, Ernesto Univ Distrital Francisco Jose Caldas Fac Ingn Proyecto Curricular Ingn Electr Bogota 110231 Colombia

This research addresses, from a perspective of metaheuristic optimization, the problem regarding parametric estimation in single-phase transformers while considering voltage and current measures at the terminals of the transformer and weighing linear loads. Transformer parametric estimation is modeled as a nonlinear problem in order to minimize the mean square error between the calculated voltage and current variables and the measurements taken. The nonlinearities are associated with Kirchhoff's first and second laws applied to the equivalent electrical circuit of the single-phase transformer. The nonlinear optimization problem is solved by applying a metaheuristic optimization algorithm known as the generalized normal distribution optimizer (GNDO), which uses evolution rules that allow exploring and exploiting the solution space via the classical probability function based on normal distributions. Numerical results in three test transformers of 20, 45, and 112.5 kVA demonstrate the effectiveness and robustness of the proposed GNDO approach when compared to other optimizers reported in the literature, such as the crow search algorithm, the coyote optimization algorithm, and the exact solution of the nonlinear optimization model using the fmincon solver of the MATLAB software. All numerical simulations confirm the potential of the GNDO approach to deal with complex optimization problems in engineering and science with promising results and low computational effort.

关键词： Nonlinear optimization metaheuristic optimization algorithms Generalized normal distribution optimizer Parameter estimation Single-phase transformers Mean square error minimization Voltage and current measurements

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Estimation of Heating Load Consumption in Residual Buildings using Optimized Regression Models Based on Support Vector Machine

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 1019-1030页

作者： Wang, Chao Qiu, Xuehui Qinhuangdao Vocat & Tech Coll Qinhuangdao 066100 Peoples R China Hebei GEO Univ Sch Urban Geol & Engn Shijiazhuang 050000 Peoples R China

Accurate energy consumption forecasting and assessing retrofit options are vital for energy conservation and emissions reduction. Predicting building energy usage is complex due to factors like building attributes, energy systems, weather conditions, and occupant behavior. Extensive research has led to diverse methods and tools for estimating building energy performance, including physics-based simulations. However, accurate simulations often require detailed data and vary based on modeling sophistication. The growing availability of public building energy data offers opportunities for applying machine learning to predict building energy performance. This study evaluates Support Vector Regression (SVR) models for estimating building heating load consumption. These models encompass a single model, one optimized with the Transit Search optimization Algorithm (TSO) and another optimized with the Coot optimization algorithm (COA). The training dataset consists of 70% of the data, which incorporates eight input variables related to the geometric and glazing characteristics of the buildings. Following the validation of 15% of the dataset, the performance of the remaining 15% is evaluated using five different assessment metrics. Among the three candidate models, Support Vector Regression optimized with the Coot optimization algorithm (SVCO) demonstrates remarkable accuracy and stability, reducing prediction errors by an average of 20% to over 50% compared to the other two models and achieving a maximum R 2 value of 0.992 for heating load prediction.

关键词： Heating load demand prediction models building energy consumption support vector machine metaheuristic optimization algorithms

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Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions

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ENERGY CONVERSION AND MANAGEMENT 2024年 301卷

作者： Refaat, Ahmed Elbaz, Ahmed Khalifa, Abd-Elwahab Elsakka, Mohamed Mohamed Kalas, Ahmed Elfar, Medhat Hegazy Port Said Univ Fac Engn Elect Engn Dept Port Said Egypt Port Said Univ Fac Engn Mech Power Engn Dept Port Said Egypt

The power generated by a proton exchange membrane fuel cell (PEMFC) is heavily impacted by the change in membrane water content (MWC) and cell temperature. Since PEMFC stacks exhibit nonlinear characteristics, it is crucial to employ a controller that can accurately track the maximum power point (MPP) and extract the most efficient power from the fuel cell (FC) stack. This article introduces a novel MPP tracking technique, based on a self-tuning particle swarm optimization (ST-PSO) algorithm, to maximize power output from PEMFC under different operational conditions. The performance of the ST-PSO algorithm is evaluated through numerical simulations and compared to four well-known metaheuristic algorithms. The results indicate that the proposed ST-PSO-based MPPT technique surpasses the other metaheuristic methods in terms of extracting the maximum power, achieving fast-tracking, and minimizing power fluctuations in various operating conditions. It attained an MPPT efficiency, consistently exceeding 99.602 % and 99.545 %, while also achieving rapid tracking times of no more than 0.366 s and 0.297 s for the two tested scenarios. Moreover, the ST-PSO controller exhibits robustness and consistent tracking of the MPP. Experimental validation of the ST-PSO controller confirms its robustness and superiority over the other tested algorithm, achieving the highest MPPT efficiency of approximately 98.94 % with a rapid tracking time of 2.0 s. Additionally, it demonstrates the lowest power fluctuations of about 2.26 %, providing a stable power output.

关键词： Fuel Cell (FC) metaheuristic optimization algorithms Self-Tuning PSO (ST-PSO) Algorithm PEMFC System MPPT Techniques

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Novel Data-Driven Machine Learning Models for Heating Load Prediction: Single and Optimized Naive Bayes

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第8期15卷 657-668页

作者： Li, Fangyuan Zhejiang Business Technol Inst Ningbo 315012 Zhejiang Peoples R China

Numerous approaches can be employed to create models for assessing the heat gains of a building arising from both external and internal sources. This modeling process evaluates effective operational strategies, conducts retrofit audits, and projects energy consumption. These techniques range from simple regression analyses to more intricate models grounded in physical principles. A prevalent assumption underlying all these modeling techniques is the requirement for input variables to be derived from authentic data, as the absence of realistic input data can lead to substantial underestimations or overestimations in energy consumption assessments. In this paper, eight input parameters, including relative compactness, orientation, wall area, roof area, glazing area, overall height, surface area, and glazing area distribution, are employed for training proposed Naive Bayes (NB)-based machine learning models. Utilizing a novel approach, this research explores the application of Beluga Whale optimization and the Coot optimization algorithm for optimizing the Naive Bayes model in heating load prediction. By harnessing the collective intelligence of Beluga Whales and drawing from the cooperative behavior of coots, the research aims to improve the model's predictive capabilities, which is of paramount importance in building energy management. Based on the comparative analysis between developed models (NB, NBCO, and NBBW), it is attainable that NBCO and NBBW, as two optimized models, have 2.4% and 1.3% higher R-2 values, respectively. Also, the RMSE of the NBCO was, on average, 19-33% lower than that of the two other models, confirming the high accuracy of NBCO. This innovative integration of bio-inspired optimization techniques with machine learning demonstrates a promising avenue for optimizing predictive models, offering potential energy efficiency and sustainability advancements in the built environment.

关键词： Prediction models heating load demand building energy consumption Naive Bayes metaheuristic optimization algorithms

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Wireless Sensor Network Coverage Problem Using Modified Fireworks Algorithm

Wireless Sensor Network Coverage Problem Using Modified Fire...

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International Wireless Communications and Mobile Computing Conference

作者： Eva Tuba Milan Tuba Dana Simian Faculty of Mathematics University of Belgrade Serbia Faculty of Computer Science John Naisbitt University Belgrade Serbia Faculty of Science Lucian Blaga University Sibiu Romania

ISBN: (纸本)9781509003051

Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it with other approaches from literature, where our algorithm proved to be very robust and better, considering all conducted tests.

关键词： Wireless sensor networks Coverage problem Fireworks algorithm Swarm intelligence metaheuristic optimization algorithms

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