Several methods exist for the determination of the efficiency of induction motors. Many of them require a no-load test, which is not possible for in-situ determination. Calculation of motor efficiency on the basis of ...
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Several methods exist for the determination of the efficiency of induction motors. Many of them require a no-load test, which is not possible for in-situ determination. Calculation of motor efficiency on the basis of a motor's nameplate or a manufacturer's data is also practicable, but these methods do not provide a correct measure of the efficiency of an induction motor in the plant. This paper discusses the application of evolutionary programming (EP) to predetermine induction motor efficiency. The validity of the proposed algorithm is tested with the help of a sample motor, and the results are found to be satisfactory compared to torque gauge results.
This paper presents a new approach using evolutionary programming for solving the economic dispatch (ED) problem of generators when some/all of the units have prohibited operating zones. In this method, additional con...
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This paper presents a new approach using evolutionary programming for solving the economic dispatch (ED) problem of generators when some/all of the units have prohibited operating zones. In this method, additional constraints such as spinning reserve requirements and ramp-rate limits are also considered besides taking into account network losses. The proposed method is implemented for solving a few example dispatch problems. The results obtained by this new approach are compared with those obtained using traditional methods. The study results have shown that the approach developed is feasible and efficient. (C) 1999 Elsevier Science S.A. All rights reserved.
This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constrain...
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This paper develops a surrogate-assisted evolutionary programming (EP) algorithm for constrained expensive black-box optimization that can be used for high-dimensional problems with many black-box inequality constraints. The proposed method does not use a penalty function and it builds surrogates for the objective and constraint functions. Each parent generates a large number of trial offspring in each generation. Then, the surrogate functions are used to identify the trial offspring that are predicted to be feasible with the best predicted objective function values or those with the minimum number of predicted constraint violations. The objective and constraint functions are then evaluated only on the most promising trial offspring from each parent, and the method proceeds in the same way as in a standard EP. In the numerical experiments, the type of surrogate used to model the objective and each of the constraint functions is a cubic radial basis function (RBF) augmented by a linear polynomial. The resulting RBF-assisted EP is applied to 18 benchmark problems and to an automotive problem with 124 decision variables and 68 black-box inequality constraints. The proposed method is much better than a traditional EP, a surrogate-assisted penalty-based EP, stochastic ranking evolution strategy, scatter search, and CMODE, and it is competitive with ConstrLMSRBF on the problems used.
evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization probl...
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evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between PEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. This paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. This paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than eras well as the better of FEP and CEP fur most benchmark problems tested.
Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage. Benefited from well-trained CNN models, small training dat...
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Convolutional Neural Network (CNN) models and many accessible large-scale public visual datasets have brought lots of research work to a remarkable new stage. Benefited from well-trained CNN models, small training datasets can learn comprehensive features by utilizing the preliminary features from transfer learning. However, the performance is not guaranteed when taking these features to construct a new model, as the differences always exist between the source and target domains. In this paper, we propose to build an Evolution programming-based framework to address various challenges. This framework automates both the feature learning and model building processes. It first identifies the most valuable features from pre-trained models and then constructs a suitable model to understand the characteristic features for different tasks. Each model differs in numerous ways. Overall, the experimental results effectively reach optimal solutions, demonstrating that a time-consuming task could also be conducted by an automated process that exceeds the human ability.
A robust Kalman filtering (KF) algorithm based on the evolutionary programming (EP) technique is proposed in this paper, for uncertain systems with unknown-but-bounded uncertain parameters which are described by inter...
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A robust Kalman filtering (KF) algorithm based on the evolutionary programming (EP) technique is proposed in this paper, for uncertain systems with unknown-but-bounded uncertain parameters which are described by interval systems. This algorithm takes advantage of the global optima-searching capability of EP to find the optimal KF results at every iteration, which include both the upper-lower boundaries and the nominal trajectory of the optimal estimates of the system state vectors. One prominent feature of this EP filtering algorithm is that it assumes the same statistical conditions and provides the same optimal estimates as the conventional KF scheme. Both linear and nonlinear systems are studied. Two typical computer simulation examples are given with comparison, which verify the merits of the new method - it yields more accurate estimation results and is less conservative as compared to the existing interval Kalman filtering (IKF). (C) 2000 Elsevier Science Inc. All rights reserved.
A new evolutionary programming using non-uniform mutation instead of Gaussian, Cauchy and Levy mutations is proposed. evolutionary programming with non-uniform mutation (NEP) has the merits of searching the space unif...
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A new evolutionary programming using non-uniform mutation instead of Gaussian, Cauchy and Levy mutations is proposed. evolutionary programming with non-uniform mutation (NEP) has the merits of searching the space uniformly at the early stage and very locally at the later stage during the programming. For a suite of 14 benchmark problems, NEP outperforms the improved evolutionary programming using mutation based on Levy probability distribution (ILEP) for multimodal functions with many local minima while being comparable to ILEP in performance for unimodal and multimodal functions with only a few minima. The detailed theoretical analysis of the executing process of NEP and the expected step size on non-uniform mutation are given. (c) 2006 Elsevier Inc. All rights reserved.
evolutionary programming has been widely implemented as a continuous optimization algorithm. Prior studies have come to a bottleneck because most of the evolutionary programming algorithms are unable to robustly solve...
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evolutionary programming has been widely implemented as a continuous optimization algorithm. Prior studies have come to a bottleneck because most of the evolutionary programming algorithms are unable to robustly solve different types of optimization problems. We argue that such a bottleneck results from the existing mutation strategies' making little use of the population information. Inspired by a psychological model which describes how a person optimizes his/her social activities by conformity behavior, this study proposes a variation vector of the mutation to simulate the conformity behavior with behavior-reference, majority-impact, and distinctive-impact factors. These factors, respectively, correspond with three types of population information for each mutated individual: heuristic information, optimal gradient, and population diversity. We use the proposed vector to design an improved evolutionary programming with a simulated-conformist mutation strategy. The results show that the population information produced by the three factors enhance the robustness of the performance of evolutionary programming in solving both uni- and multimodal functions. The finding is verified by empirical analyses of two sets of benchmark functions proposed in 1998 and 2013. The numerical results indicate that the proposed algorithm performs significantly better on average than the existing EPs and some other algorithms with similar strategies.
In this paper, an evolutionary programming (EP) based technique has been presented for the optimal placement of distributed generation (DG) units energized by renewable energy resources (wind and solar) in a radial di...
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In this paper, an evolutionary programming (EP) based technique has been presented for the optimal placement of distributed generation (DG) units energized by renewable energy resources (wind and solar) in a radial distribution system. The correlation between load and renewable resources has been nullified by dividing the study period into several segments and treating each segment independently. To handle the uncertainties associated with load and renewable resources, probabilistic techniques have been used. Two operation strategies, namely "turning off wind turbine generator" and "clipping wind turbine generator output", have also been adopted to restrict the wind power dispatch to a specified fraction of system load for system stability consideration. To reduce the search space and thereby to minimize the computational burden, a sensitivity analysis technique has been employed which gives a set of locations suitable for DG placement. For the proposed EP based approach, an index based scheme has also been developed to generate the population ensuring the feasibility of each individual and thus considerably reducing the computational time. The developed technique has been applied to a 12.66-kV, 69-bus distribution test system. The solutions result in significant loss reduction and voltage profile improvement.
This article presents the solution of optimal power flow (OPF) problem of generator units with ramp rate limits and non-smooth fuel functions. In this article evolutionary programming (EP) algorithm is devoted to solv...
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This article presents the solution of optimal power flow (OPF) problem of generator units with ramp rate limits and non-smooth fuel functions. In this article evolutionary programming (EP) algorithm is devoted to solve the OPF problem with non-smooth fuel cost functions like quadratic, piece-wise, valve point loading and combined cycle cogeneration plants. In the proposed EP algorithm, mutation is changing non-linearly with respect to the number of generations to avoid premature condition. The proposed EP algorithm is demonstrated to solve OPF problem for IEEE-30 bus system and Indian utility-62 bus system with line flow constraints. The line flows in MVA are computed directly from Newton Raphson method The test results prove that the EP method is simpler and more efficient for solving OPF problem with non-smooth fuel cost functions with many constraints.
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