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27th International Conference on Radioelektronika (RADIOELEKTRONIKA)

作者： Tuba, Eva Tuba, Milan Beko, Marko Univ Belgrade Fac Math Belgrade Serbia John Naisbitt Univ Grad Sch Comp Sci Belgrade Serbia Univ Lusofona Humanidades & Tecnol Comp Engn Dept Lisabon Portugal

ISBN: (纸本)9781509045914

Wireless sensor networks have many applications and accordingly represent an active research area. Coverage maximization of the area of interest by sensors that have limited sensing radius is an important hard optimization problem. Since sensors are initially often deployed randomly one way of solving maximal coverage problem is by using mobile sensors that move to optimal positions. Since power in sensor nodes is limited, minimization of the sensor nodes movement is secondary optimization goal. In this paper we propose use of recent swarm intelligence algorithm, firefly algorithm, for optimization of that hard multiobjective problem. We tested our approach on standard benchmark data and compared results with other techniques from literature. Our proposed approach was better considering all quality measures: coverage, energy consumption, robustness and convergence speed.

关键词： wireless sensor networks mobile sensors coverage problem firefly algorithm swarm intelligence metaheuristic optimization algorithms

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Energy Efficient Sink Placement in Wireless Sensor Networks by Brain Storm optimization Algorithm 14

Energy Efficient Sink Placement in Wireless Sensor Networks ...

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14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Tuba, Eva Simian, Dana Dolicanin, Edin Jovanovic, Raka Tuba, Milan Singidunum Univ Fac Informat & Comp Belgrade Serbia Lucian Blaga Univ Fac Sci Sibiu Romania State Univ Novi Pazar Dept Tech Sci Novi Pazar Serbia Hamad bin Khalifa Univ Qatar Environm & Energy Res Inst Doha Qatar

ISBN: (纸本)9781538620700

Wireless sensor networks represent one of the most promising technologies whose use has significantly increased in the past years. They are used in various applications such as health care monitoring, surveillance and monitoring in agriculture, industrial monitoring, habitat and underwater monitoring, etc. Deployment of the wireless sensor networks introduces number of hard optimization problems. Placement of the elements such as sensors, gateways, sinks and base stations, depend on different conditions and constraints such as signal propagation, distance, energy preservation, reliability. In this paper, we propose a method based on brain storm optimization algorithm for placing multiple sinks in a network consisting of regular sensors and gateways with higher battery capacity. Regular sensor nodes are statically organized in clusters around gateways considering not only distance but also energy efficiency. Gateways communicate with sinks for which optimal positions need to be determined. Location problems are in general difficult and in this case requirement of minimizing the energy additionally complicates the problem. The simulation results report estimation of the network life time. Obtained results have shown that our proposed method outperformed particle swarm optimization based method from literature in terms of mentioned metrics.

关键词： Wireless sensor networks Sink placement Brain storm optimization algorithm Swarm intelligence metaheuristic optimization algorithms

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AN ENHANCED IMPERIALIST COMPETITIVE ALGORITHM FOR OPTIMUM DESIGN OF STEEL FRAMES 8

AN ENHANCED IMPERIALIST COMPETITIVE ALGORITHM FOR OPTIMUM DE...

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8th International Structural Engineering and Construction Conference on - Implementing Innovative Ideas in Structural Engineering Project Management

作者： Maheri, Mahmoud R. Talezadeh, M. Shiraz Univ Dept Civil Engn Shiraz Iran

ISBN: (纸本)9780996043717

Development of efficient and robust optimization methods for structural design is one of the most active research fields in structural engineering. Imperialist Competitive Algorithm (ICA) is one of the recent meta-heuristic algorithms proposed to solve optimization problems. In this paper, an Enhanced Imperialist Competitive Algorithm (EICA) is proposed which increases the search space and enables the ICA algorithm to escape from local optima in a fast time. In this algorithm added value is given to a slightly unfeasible solution, based on its distance from the relative imperialist. The performance of the proposed EICA algorithm in optimum design of side sway frames is investigated by comparing the EICA optimum designs of two benchmark side sway frames with the best designs obtained using a number of other meta-heuristic solutions. Results indicate that, in terms of both the design quality and the solution speed, EICA compares favorably with a number of other meta-heuristic optimizers, including the basic ICA.

关键词： Design optimization metaheuristic optimization algorithms Skeletal structures Global optima Search space

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CONCEPTUAL AERODYNAMIC DESIGN OF RAMJET MISSILES

CONCEPTUAL AERODYNAMIC DESIGN OF RAMJET MISSILES

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作者： ERTAN DEMIRAL MIDDLE EAST TECHNICAL UNIVERSITY

学位级别：硕士

The prediction of aerodynamic coefficients of missiles that have air-breathing components is a challenging task during the preliminary design phase. It is considerably important to estimate missile aerodynamic coefficients accurately at the beginning of design phase to avoid poor designs which could lead to redesign at later stages of the design process. In this study, firstly an improved method is developed to predict the aerodynamic coefficients of missile configurations with air-breathing components more accurately compared to engineering level fast prediction tools. Then, optimization studies comprising of reaching global minimum value of the Beale Function and inverse design optimization of the ramjet missile configuration are done through implementing different meta-heuristic optimization techniques which are "Genetic Algorithm", "Differential Evolution" and "Modified Cuckoo Search". In the inverse design optimization studies, two different methods, which are engineering level fast prediction tool Missile DATCOM and the improved method, are used to calculate aerodynamic coefficients of the candidate missile configurations throughout the design optimization process. Results of the inverse design optimization studies show that preliminary design phase of the missiles including air-breathing components can be enhanced significantly since accuracy of the prediction of aerodynamic coefficients is improved by methods applied in this study.

关键词： Ramjet Missiles metaheuristic optimization algorithms Computational Fluid Dynamics Fast Prediction Tools Component Build Up Method

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Aplicação de algoritmos de otimização metaheurísticos na estimativa dos parâmetros ótimos de diferentes distribuições de velocidade do vento em duas cidades do nordeste brasileiro

Aplicação de algoritmos de otimização metaheurísticos n...

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作者： Guedes, Kevin Santos Universidade de Lisboa

For a better use of wind energy, a thorough analysis of the wind resource is essential. In this analysis, wind speed distributions play an important role, the most common being the Weibull distribution. However, this distribution is not always the most suitable, which makes it necessary to evaluate different distributions to obtain more reliable information. Another essential step is the estimation of the parameters governing these distributions, as the accuracy of these estimates directly affects the power generation calculations. In the last years, different optimization methods have been used for this purpose and, compared to traditional numerical methods, they have performed better. However, in wind energy, the application of these methods is centered on the conventional two-parameters distributions, such as Weibull, Gamma and Lognormal. Moreover, different authors emphasize the lack of studies that use optimization methods for this purpose. Given this scenario, four metaheuristic optimization algorithms (MOA), namely, Migrating Birds, Imperialist Competition, Harmony Search and Cuckoo Search, were used in this study to fit 11 wind speed distributions. The study was conducted in two cities in Northeastern Brazil, which is one of the best regions in the world for wind energy generation, since winds are favorable for this purpose. Three distinct objective functions were also analyzed to determine which one should be applied to the MOA in order to obtain a better fit. Finally, the fits obtained by the MOA were compared, through an objective statistical analysis, with those obtained by the Maximum Likelihood (MLE) numerical method. The objective function that achieved best results was the maximization of the determination coefficient. Regarding the MLE, the MOA presented significantly better fits, since the Global Score (GS) values obtained were lower. This demonstrates the ability of MOA to accurately estimate the parameters of the distribution models. In both anal

关键词： Engenharia mecânica Ventos Energia eólica - Otimização Multi-criteria statistical analysis Wind speed modeling metaheuristic optimization algorithms Dissertação

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Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

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ENERGIES 2022年第21期15卷 7929页

作者： Arvanitidis, Athanasios Ioannis Bargiotas, Dimitrios Kontogiannis, Dimitrios Fevgas, Athanasios Alamaniotis, Miltiadis Univ Thessaly Dept Elect & Comp Engn Volos 38334 Greece Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA

In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company's decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm optimization, with a mean absolute percentage error value of 6.15%.

关键词： short-term electricity price forecasting data-driven forecasting models metaheuristic optimization algorithms signal decomposition clustering algorithms preprocessing approaches

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An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design optimization

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9610页

作者： Furio, Chiara Lamberti, Luciano Pruncu, Catalin I. Polytech Univ Bari Dept Mech Math & Management Via Edoardo Orabona4 I-70125 Bari Italy Buckinghamshire New Univ Sch Engn & Built Environm 59 Walton St Aylesbury HP21 7OG England

metaheuristic algorithms (MHAs) are widely used in engineering applications in view of their global optimization capability. Researchers continuously develop new MHAs trying to improve the computational efficiency of optimization search. However, most of the newly proposed algorithms rapidly lost their attractiveness right after their release. In the present study, two classical and powerful MHAs, namely the grey wolf optimizer (GWO) and the JAYA algorithm, which still attract the attention of optimization experts, were combined into a new hybrid algorithm called FHGWJA (Fast Hybrid Grey Wolf JAYA). FHGWJA utilized elitist strategies and repairing schemes to generate high-quality new trial solutions that may always improve the current best record or at least the old population. The proposed FHGWJA algorithm was successfully tested in seven engineering optimization problems formulated in the fields of robotics, hydraulics, and mechanical and civil engineering. Design examples included up to 29 optimization variables and 1200 nonlinear constraints. The optimization results proved that FHGWJA always was superior or very competitive with the other state-of-the-art MHAs including other GWO and JAYA variants. In fact, FHGWJA always converged to the global optimum and very often achieved 0 or nearly 0 standard deviation, with all optimization runs practically converging to the target design. Furthermore, FHGWJA always ranked 1st or 2nd in terms of average computational speed, and its fastest optimization runs were better or highly competitive with those of the best MHA taken for comparison.

关键词： metaheuristic optimization algorithms fast hybrid optimization algorithms GWO JAYA elitist strategies engineering problems

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A novel benchmark shading pattern for PV maximum power point trackers evaluation

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SOLAR ENERGY 2023年第1期263卷

作者： Eltamaly, Ali M. King Saud Univ Sustainable Energy Technol Ctr Riyadh 11421 Saudi Arabia

Partial shading conditions (PSC) drive the P-V characteristics of the PV system to have multiple local peaks (LPs) and one global peak (GP). Conventional maximum power point tracker (MPPT) techniques may be trapped in one of the LPs and miss the GP, which substantially reduces the efficiency of the PV systems. metaheuristic opti-mization algorithms (MOAs) have been widely used recently to avoid this limitation. Although, the robustness of the MOAs in tracking the GP, still they have many challenges that should be overcome. This paper shed light on the most important problems and solutions provided associated with the use of MOAs as an MPPT of PV systems. Moreover, this paper introduced benchmark shading patterns (BSP) that contain most of the important chal-lenges that may occur in the PSC of PV systems. This BSP can be used as an evaluation benchmark for all MPPT techniques. The problem of stagnation of search agents within a certain LP is investigated and solutions are provided and evaluated. A novel flying search agent (FSA) strategy is used to detect any change in GP position without sacrificing the stability or the ripple contents in the PV system waveforms. The proposed FSA sends a search agent to the position of the anticipated peaks and compares its power with the current GP power. If the power generated from the FSA is higher than the current GP, it will move the particles to search around the position of the FSA.

关键词： MPPT PV systems metaheuristic optimization algorithms Benchmark shading patterns Global peak Local peaks

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A novel study on power consumption of an HVAC system using CatBoost and AdaBoost algorithms combined with the metaheuristic algorithms

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ENERGY 2024年 302卷

作者： Bian, Jianxiao Wang, Jiarui Yece, Qian Longdong Univ Sch Intelligent Mfg Qingyang Peoples R China Nanjing Normal Univ Sch Elect & Automat Engn Nanjing Peoples R China Chizhou Univ Coll Mech & Elect Engn Chizhou 247000 Peoples R China

Efficient power management is fundamental for organizations and systems to conserve resources, reduce costs, and improve environmental sustainability. To achieve the mentioned purposes, it is vital to perceive power consumption patterns and trends to make informed decisions about energy usage. By evaluating power consumption, inefficiencies and areas for improvement can be identified. In this regard, this paper presents a comprehensive study to predict power usage in a non-residential building HVAC system. The study employs the integration of machine learning models and optimization algorithms to achieve accurate predictions. The results demonstrate the effectiveness of the approach, with the CatBoost-AO model depicting superior performance in comparison with other models across a range of statistical evaluation metrics including NSE, JSD, RMSE, MAPE, VAF, KLD, as well as R 2 and runtime. Remarkably, the CatBoost-AO model achieves the maximum R 2 value of 0.91, indicating strong predictive capability. Overall, the presented study highlights the potential of employing machine learning and optimization techniques to improve power management and resource efficiency in HVAC systems, contributing to more sustainable and cost-effective operations in non-residential buildings.

关键词： Power consumption prediction HVAC systems Machine learning CatBoost model metaheuristic optimization algorithms

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An Integrated Model for Multi-Mode Resource-Constrained Multi-Project Scheduling Problems Considering Supply Management with Sustainable Approach in the Construction Industry under Uncertainty Using Evidence Theory and optimization algorithms

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BUILDINGS 2023年第8期13卷

作者： Ghoroqi, Mahyar Ghoddousi, Parviz Makui, Ahmad Javid, Ali Akbar Shirzadi Talebi, Saeed Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran Iran Univ Sci & Technol Sch Ind Engn Tehran *** Iran Birmingham City Univ Coll Built Environm Fac Comp Engn & Built Environm Birmingham B4 7XG England

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors;the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model's performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample prob

关键词： multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) multi-objective mathematical modeling metaheuristic optimization algorithms uncertainty evidence theory sustainability

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