Search

Wind energy is a very popular renewable energy resource and is used as an energy source global because of its benefits of being environmentally friendly, renewable and having great reserves. The probability density distribution of wind speed can be used to estimate wind power density. In this study, Weibull and Rayleigh density distributions were employed to analytically eliminate the presumption that the total wind power is described by a single random variant and to calculate the wind power probability density distribution. In the modeling of complex high-dimensional stochastic wind power, although it can be solved with various mathematical approaches, since there are generally large-scale power systems containing many generators, buses, planning periods and non-linear stochastic variables, it is quite leisurely in searching for the optimum point and most of the time the solutions are far from reality. Consequently, heuristic methods have now substituted classical mathematical methods in obtaining wind parameters. Therefore, the advantage of heuristic methods compared to classical methods is that they can produce efficient solutions in a shorter time and with greater precision. Therefore, in this study, the main metaheuristic algorithms Symbiosis Organisms Search (SOS) and Artificial Bee Colony (ABC) algorithms and the classical statistical methods Energy Pattern Factor and Maximum Likelihood Method were employed to investigate the accuracy of wind power parameter calculations. [ABSTRACT FROM AUTHOR]

This paper presents an improved version of Linear Population Size Reduction Success History Based Adaptive Differential Evolution (LSHADE) algorithm for solving global optimization problems. Fitness Distance Balance (FDB) selection method was used to redesign the mutation operator in order to enhance the search performance of the LSHADE algorithm. In order to test and validate the performance of the proposed algorithm, a comprehensive experimental study was carried out. For this purpose, it was tested on the CEC14 and CEC17 benchmark problems, consisting of different problem types and dimensions. Results of the FDB-LSHADE was compared to the performance of 8 other up-to-date and highly preferred metaheuristic search (MHS) algorithms. According to Friedman test results, the proposed FDBLSHADE algorithm ranked first among the all competing algorithms. Moreover, the proposed algorithm was used to solve single- and multi-objective energy hub economic dispatch (EHED) problems, which were a non-convex, a nonlinear, and high dimensional problems. To analyze the results of the proposed algorithm obtained from experimental studies, two non-parametric statistical methods, which are Wilcoxon and Friedman tests, were used. The simulation results of the proposed algorithm were compared to the results of the 8 other MHS algorithms. The results demonstrated that the FDB-LSHADE was a superior performance compared to other MHS algorithms for solving both benchmark and EHED problems.

Computational intelligence and machine learning techniques have been widely considered for a variety of domains, including financial and data analysis applications. Stock market trading, as a part of the financial domain, has benefited from these techniques in learning models to forecast the direction of stock prices. Traders typically rely on these models, trained using historical stock market data, for monitoring market events to make the right decisions, which can result in more profits while exchanging stocks. One observed limitation of the practices currently being utilized for learning stock market models is the use of manually labeled datasets. This reduces the effectiveness of the data labeling and can cause a lower model prediction accuracy. To address this limitation, this paper proposes an automatic labeling approach that exploits a metaheuristic search to perform the labeling task for stock market data. The results of empirical experiments demonstrate that this approach is very promising as it outperforms the current manual approaches for stock data labeling and achieves higher labeling effectiveness.

Electric variable speed drives (VSD) have been replacing mechanic and hydraulic systems in many sectors of industry and transportation because of their better performance and reduced cost. However, the electric systems still face the issue of being considered less reliable than the mechanical ones. For this reason, researchers have been actively investigating effective ways to increase the reliability of such systems. This paper is focused on the analysis of the common-mode voltage (CMV) generated by the operation of the VSDs which directly affects to the lifetime and reliability of the complete system. The method is based on the mathematical description of the harmonic spectrum of the CMV depending on the PWM method implementation. A generalized PWM method where the carriers present a variable phase-displacement is developed. As a result of the presented analysis, the CMV reduction is achieved by applying the PWM method with optimal carrier phase-displacement angles without any external component and/or passive filtering technique. The optimal values of the carrier phase-displacement angles are obtained considering the minimization of the CMV total harmonic distortion. The resulting method is easily implementable on mostly off-the-shelf mid-range micro-controller control platforms. The strategy has been evaluated in a scaled-down experimental setup proving its good performance.

Hand-in-hand with the smart-grid paradigm development, power converters used in high-power applications are facing important challenges related to efficiency and power quality. To overcome these issues, the pre-programmed Pulse-Width Modulation (PWM) methods have been extensively applied to reduce the harmonic distortion with very low power switching losses for high-power converters. Among the pre-programmed PWM techniques, Selective Harmonic Elimination (SHE) has been the prevailing solution, but recently, Selective Harmonic Mitigation (SHM) stands as a superior alternative to provide further control of the harmonic spectrum with similar losses. However, the large computational burden required by the SHM method to find a solution confines it as an off-line application, where the switching set valid solutions are pre-computed and stored in a memory. In this paper, for the first time, a real-time implementation of SHM using an off-the-shelf mid-range microcontroller is presented and tested. The Exchange Market Algorithm (EMA), initially focused on optimizing financial transactions, is considered and executed to achieve the SHM targets. The performance of the EMA-based SHM is presented showing experimental results considering a reduced number of switching angles applied to a specific three-level converter, but the method can be extrapolated to any other three-level converter topology.

The firefly algorithm (FA), as a metaheuristic search method, is useful for solving diverse optimization problems. However, it is challenging to use FA in tackling high dimensional optimization problems, and the random movement of FA has a high likelihood to be trapped in local optima. In this research, we propose three improved algorithms, i.e., Repulsive Firefly Algorithm (RFA), Scattering Repulsive Firefly Algorithm (SRFA), and Enhanced SRFA (ESRFA), to mitigate the premature convergence problem of the original FA model. RFA adopts a repulsive force strategy to accelerate fireflies (i.e. solutions) to move away from unpromising search regions, in order to reach global optimality in fewer iterations. SRFA employs a scattering mechanism along with the repulsive force strategy to divert weak neighbouring solutions to new search regions, in order to increase global exploration. Motivated by the survival tactics of hawk-moths, ESRFA incorporates a hovering-driven attractiveness operation, an exploration-driven evading mechanism, and a learning scheme based on the historical best experience in the neighbourhood to further enhance SRFA. Standard and CEC2014 benchmark optimization functions are used for evaluation of the proposed FA-based models. The empirical results indicate that ESRFA, SRFA and RFA significantly outperform the original FA model, a number of state-of-the-art FA variants, and other swarm-based algorithms, which include Simulated Annealing, Cuckoo Search, Particle Swarm, Bat Swarm, Dragonfly, and Ant-Lion Optimization, in diverse challenging benchmark functions. [ABSTRACT FROM AUTHOR]

Heat transfer search (HTS) is a novel metaheuristic optimization algorithm that simulates the laws of thermodynamics and heat transfer. In this study, the HTS algorithm is adapted to truss structure optimization. Sizing optimization searches for the minimum weight of a structure subject to stress and displacement constraints. Three truss structures often taken as benchmarks in the optimization literature are selected here in order to verify the efficiency and robustness of the HTS algorithm. Optimization results indicate that HTS can obtain better designs (i.e. lighter trusses) than most of the state-of-the-art metaheuristic optimizers. The convergence behaviour of HTS also is as good as the other algorithms. [ABSTRACT FROM AUTHOR]

As research in the area of model predictive control (MPC) for building energy systems intensifies, appropriate methods are required to model a building's thermodynamic properties. In this paper, building models are considered from two perspectives – simulation and optimisation. First, a methodology is devised for the development of complex simulation models for control strategy comparison and analysis. A novel spatio-temporal filtering technique for estimation of disturbances is devised and combined with metaheuristic search methods to allow for models to be derived from data in which typical disturbances are present. Adapting the disturbance estimation techniques, methods are then proposed for deriving low-order models from data, suitable for use within an optimisation-based MPC strategy. The modelling concepts are implemented using data from a real building and a potential MPC formulation is assessed. [ABSTRACT FROM AUTHOR]

Abstract: There is a noticeable increase in the emergence of non-deterministic search techniques that simulate natural phenomena into a numerical optimization technique in recent years. These techniques are used for developing structural optimization algorithms that are particularly effective for obtaining solutions to discrete programming problems. In this study amongst these techniques genetic algorithms, simulated annealing, evolution strategies, particle swarm optimizer, tabu search, ant colony optimization and harmony search are utilized to develop seven optimum design algorithms for real size rigidly connected steel frames. The design problems are formulated according to ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution). [Copyright &y& Elsevier]

Electric variable speed drives (VSD) have been replacing mechanic and hydraulic systems in many sectors of industry and transportation because of their better performance and reduced cost. However, the electric systems still face the issue of being considered less reliable than the mechanical ones. For this reason, researchers have been actively investigating effective ways to increase the reliability of such systems. This paper is focused on the analysis of the common-mode voltage (CMV) generated by the operation of the VSDs which directly affects to the lifetime and reliability of the complete system. The method is based on the mathematical description of the harmonic spectrum of the CMV depending on the PWM method implementation. A generalized PWM method where the carriers present a variable phase-displacement is developed. As a result of the presented analysis, the CMV reduction is achieved by applying the PWM method with optimal carrier phase-displacement angles without any external component and/or passive filtering technique. The optimal values of the carrier phase-displacement angles are obtained considering the minimization of the CMV total harmonic distortion. The resulting method is easily implementable on mostly off-the-shelf mid-range micro-controller control platforms. The strategy has been evaluated in a scaled-down experimental setup proving its good performance. [ABSTRACT FROM AUTHOR]

Feature selection plays an important role to build a successful speech emotion recognition system. In this paper, a feature selection approach which modifies the initial population generation stage of metaheuristic search algorithms, is proposed. The approach is evaluated on two metaheuristic search algorithms, a nondominated sorting genetic algorithm-II (NSGA-II) and Cuckoo Search in the context of speech emotion recognition using Berlin emotional speech database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) database. Results show that the presented feature selection algorithms reduce the number of features significantly and are still effective for emotion classification from speech. Specifically, in speaker-dependent experiments of the EMO-DB, recognition rates of 87.66% and 87.20% are obtained using selected features by modified Cuckoo Search and NSGA-II respectively, whereas, for the IEMOCAP database, the accuracies of 69.30% and 68.32% are obtained using SVM classifier. For the speaker-independent experiments, we achieved comparable results for both databases. Specifically, recognition rates of 76.80% and 76.82% for EMO-DB and 59.37% and 59.52% for IEMOCAP using modified NSGA-II and Cuckoo Search respectively. [ABSTRACT FROM AUTHOR]

Hand-in-hand with the smart-grid paradigm development, power converters used in high-power applications are facing important challenges related to efficiency and power quality. To overcome these issues, the pre-programmed Pulse-Width Modulation (PWM) methods have been extensively applied to reduce the harmonic distortion with very low power switching losses for high-power converters. Among the pre-programmed PWM techniques, Selective Harmonic Elimination (SHE) has been the prevailing solution, but recently, Selective Harmonic Mitigation (SHM) stands as a superior alternative to provide further control of the harmonic spectrum with similar losses. However, the large computational burden required by the SHM method to find a solution confines it as an off-line application, where the switching set valid solutions are pre-computed and stored in a memory. In this paper, for the first time, a real-time implementation of SHM using an off-the-shelf mid-range microcontroller is presented and tested. The Exchange Market Algorithm (EMA), initially focused on optimizing financial transactions, is considered and executed to achieve the SHM targets. The performance of the EMA-based SHM is presented showing experimental results considering a reduced number of switching angles applied to a specific three-level converter, but the method can be extrapolated to any other three-level converter topology. [ABSTRACT FROM AUTHOR]

Abstract Heat transfer search (HTS) is a novel metaheuristic optimization algorithm that simulates the laws of thermodynamics and heat transfer. In this study, the HTS algorithm is adapted to truss structure optimization. Sizing optimization searches for the minimum weight of a structure subject to stress and displacement constraints. Three truss structures often taken as benchmarks in the optimization literature are selected here in order to verify the efficiency and robustness of the HTS algorithm. Optimization results indicate that HTS can obtain better designs (i.e. lighter trusses) than most of the state-of-the-art metaheuristic optimizers. The convergence behaviour of HTS also is as good as the other algorithms.