A two-level, two-objective optimization scheme based on evolutionary algorithms (EAs) is proposed for solving power generating Unit Commitment (UC) problems by considering stochastic power demand variations. Apart fro...
详细信息
A two-level, two-objective optimization scheme based on evolutionary algorithms (EAs) is proposed for solving power generating Unit Commitment (UC) problems by considering stochastic power demand variations. Apart from the total operating cost to cover a known power demand distribution over the scheduling horizon, which is the first objective, the risk of not fulfilling possible demand variations forms the second objective to be minimized. For this kind of problems with a high number of decision variables, conventional EAs become inefficient optimization tools, since they require a high number of evaluations before reaching the optimal solution(s). To considerably reduce the computational burden, a two-level algorithm is proposed. At the low level, a coarsened UC problem is defined and solved using EAs to locate promising solutions at low cost: a strategy for coarsening the UC problem is proposed. Promising solutions migrate upwards to be injected into the high level EA population for further refinement. In addition, at the high level, the scheduling horizon is partitioned in a small number of subperiods of time which are optimized iteratively using EAs, based on objective function(s) penalized to ensure smooth transition from/to the adjacent subperiods. Handling shorter chromosomes due to partitioning increases method's efficiency despite the need for iterating. The proposed two-level method and conventional EAs are compared on representative test problems. (C) 2008 Elsevier Ltd. All rights reserved.
In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of ...
详细信息
In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods.
This article examines the effect of different configuration issues of the Multiobjective evolutionary algorithms on the efficient frontier formulation for the constrained portfolio optimization problem. We present the...
详细信息
This article examines the effect of different configuration issues of the Multiobjective evolutionary algorithms on the efficient frontier formulation for the constrained portfolio optimization problem. We present the most popular techniques for dealing with the complexities of the constrained portfolio optimization problem and experimentally analyse their strengths and weaknesses. In particular, we examine the efficient incorporation of complex real world constraints into the Multiobjective evolutionary algorithms and their corresponding effect on the efficient frontier formulation for the portfolio optimization problem. Moreover, we examine various constraint-handling approaches for the constrained portfolio optimization problem such as penalty functions and reparation operators and we draw conclusions about the efficacy of the examined approaches. We also examine the effect on the efficient frontier formulation by the application of different genetic operators and the relevant results are analysed. Finally, we address issues related with the various performance metrics that are applied for the evaluation of the derived solutions.
Purpose - The purpose of this paper is to obtain a preliminary off-line identification of induction motor (IM) parameters at standstill in a reasonable calculation time, which will be useful for the initial adjustment...
详细信息
Purpose - The purpose of this paper is to obtain a preliminary off-line identification of induction motor (IM) parameters at standstill in a reasonable calculation time, which will be useful for the initial adjustment of controllers and state observer parameters in the sensorless drive system. Design/methodology/approach - The identification procedure of electrical parameters of IM equivalent circuit is performed at standstill and is based on the reconstruction of the stator current response to the forced stator voltage using evolutionary algorithms (EAs) with hard selection and different mutation schemes. Findings - It is shown that an application of the EA with adaptive mutation mechanism based on simulated annealing method gives very good accuracy of parameters identification and the shortest execution time of the identification procedure as well in Simulation as in the experimental tests. Research limitations/implications - The investigation looks mainly at the minimization of the execution time of the identification algorithm and on the identification accuracy performance, taking into account the good approximation of the measured stator current response. Practical implications - The proposed EA with the improved adaptive mutation scheme can be easily realised using modem digital signal processor (DSP), which is usually applied for control purposes of the sensorless IM drive system with vector control. The implementation is tested in experimental setup with floating point DSP used as the system controller. Originality/value - The application of adaptive mutation with simulated annealing in the EA with hard selection for the fast, off-line preliminary identification of the IM parameter at standstill.
The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a gen...
详细信息
The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.
Many chaotic dynamical systems can produce time series with a wide range of temporal and spectral properties as a function of only a few fixed parameters. This malleability invites their use as tools for shaping or de...
详细信息
Many chaotic dynamical systems can produce time series with a wide range of temporal and spectral properties as a function of only a few fixed parameters. This malleability invites their use as tools for shaping or designing inputs used to drive a separate dynamical system of interest. By specifying an objective function and employing an evolutionary algorithm to manipulate the parameters governing the dynamics of the forcing system, the output of the driven system is made to approach an optimal response subject to desired constraints. The technique's versatility is demonstrated for two different applications: damage detection in structures and phase-locked loop disruption.
In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft'...
详细信息
In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft's mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H-infinity method and two different metaheuristic algorithms;in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H 1 weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft's flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was co
The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and interesting areas of research in evolutionary computation. In this paper we propose two new parameter...
详细信息
The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and interesting areas of research in evolutionary computation. In this paper we propose two new parameter control strategies for evolutionary algorithms based on the ideas of reinforcement learning. These strategies provide efficient and low-cost adaptive techniques for parameter control and they preserve the original design of the evolutionary algorithm, as they can be included without changing either the structure of the algorithm nor its operators design. (C) 2010 Elsevier Inc. All rights reserved.
Following the rapid growth in accelerator-based light sources research since the mid of 20th century, miscellaneous third generation synchrotron radiation (SR) facilities such as SSRL, APS, ESRF, PETRA-III, and SPring...
详细信息
Following the rapid growth in accelerator-based light sources research since the mid of 20th century, miscellaneous third generation synchrotron radiation (SR) facilities such as SSRL, APS, ESRF, PETRA-III, and SPring-8 have come into existence. These SR source facilities provide 1020-1025 photons/s/mrad2/mm2/0.1%BW peak brightness within the photon energy range of 10-105 eV. Since different measurement techniques are utilized at X-ray beamlines of SR facilities, many kinds of insertion devices (i.e., undulators and wigglers) and optical components (e.g., high-resolution monochromators, double-crystal monochromators, lenses, mirrors, etc.) are employed for each experimental setup as a matter of course. Under the circumstances, optimization of a synchrotron beamline is a big concern for many scientists to ensure required radiation characteristics (i.e., photon flux, spot size, photon energy, etc.) for dedicated user experiments. In this respect, an in-vacuum hybrid undulator driven by a 6 GeV synchrotron electron beam is optimized using evolutionary algorithms (EA). Finally, it is shown that EA results are well consistent with both the literature and the analytical calculations, resulting in a promising design estimation for beamline scientists.
Topology optimization has evolved rapidly since the late 1980s. The optimization of the geometry and topology of structures has a great impact on its performance, and the last two decades have seen an exponential incr...
详细信息
Topology optimization has evolved rapidly since the late 1980s. The optimization of the geometry and topology of structures has a great impact on its performance, and the last two decades have seen an exponential increase in publications on structural optimization. This has mainly been due to the success of material distribution methods, originating in 1988, for generating optimal topologies of structural elements. Previous methods suffered from mathematical complexity and a limited scope for applicability, however with the advent of increased computational power and new techniques topology optimization has grown into a design tool used by industry. There are two main fields in structural topology optimization, gradient based, where mathematical models are derived to calculate the sensitivities of the design variables, and non gradient based, where material is removed or included using a sensitivity function. Both fields have been researched in great detail over the last two decades, to the point where structural topology optimization has been applied to real world structures. It is the objective of this review paper to present an overview of the developments in non gradient based structural topology and shape optimization, with a focus on evolutionary algorithms, which began as a non gradient method, but have developed to incorporate gradient based techniques. Starting with the early work and development of the popular algorithms and focusing on the various applications. The sensitivity functions for various optimization tasks are presented and real world applications are analyzed. The article concludes with new applications of topology optimization and applications in various engineering fields.
暂无评论