This paper presents a contingency constrained economic load dispatch (CCELD) using proposed improved particle swarm optimization (IPSO), conventional particle swarm optimization (PSO). evolutionary programming(EP) tec...
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This paper presents a contingency constrained economic load dispatch (CCELD) using proposed improved particle swarm optimization (IPSO), conventional particle swarm optimization (PSO). evolutionary programming(EP) techniques such as classical EP(CEP), fast-EP (FEP) and mean of classical and fast EP (MFEP) to alleviate line overloading. Power system security enhancement deals with the task of taking remedial action against possible network overloads in the system following the occurrences of contingencies. Line overload can be removed by means of generation redispatching. In the proposed improved PSO, a new velocity strategy equation with scaling factor is proposed and the constriction factor approach (CFA) utilizes the eigen value analysis and controls the system behaviour. The CCELD problem is a twin objective function viz. minimization of fuel cost and minimization of severity index. This proposed IPSO-based CCELD approach generates higher quality solution in terms of optimal cost, minimum CPU time and minimum severity index than the other methods. Simulation results on IEEE-118 bus and IEEE-30 bus test systems are presented and compared with the results of other approaches. (C) 2008 Elsevier B.V. All rights reserved.
Based on the metaphor of the foraging mechanism of honey bee swarms as well as on available works, an artificial bee colony algorithm is proposed for solving difficult inverse electromagnetic problems. In the proposed...
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Based on the metaphor of the foraging mechanism of honey bee swarms as well as on available works, an artificial bee colony algorithm is proposed for solving difficult inverse electromagnetic problems. In the proposed algorithm, the entire searching process is divided into an intensification and a diversification phase. For intensification searches, some novel formulas are proposed for the employed bees and onlookers to carry out exploiting searches around specific memorized food sources;and scouts are used to generate new food sources to guarantee the diversity of the algorithm in diversification searches. Also, an age variable is introduced to measure the "exhausted" level of a food source and then decide when to abandon a memorized food source. Three numerical examples are reported to validate the robustness and to demonstrate the advantages of the proposed algorithm.
This paper presents a methodology for finding the optimal output power from a PEM fuel cell power plant (FCPP). The FCPP is used to supply power to a small micro-grid community. The technique used is based on evolutio...
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This paper presents a methodology for finding the optimal output power from a PEM fuel cell power plant (FCPP). The FCPP is used to supply power to a small micro-grid community. The technique used is based on evolutionary programming (EP) to find a near-optimal solution of the problem. The method incorporates the Hill-Climbing technique (HCT) to maintain feasibility during the solution process. An economic model of the FCPP is used. The model considers the production cost of energy and the possibility of selling and buying electrical energy from the local grid. In addition, the model takes into account the thermal energy output from the FCPP and the thermal energy requirement for the micro-grid community. The results obtained are compared against a solution based on genetic algorithms. Results are encouraging and indicate viability of the proposed technique. (C) 2004 Elsevier B.V. All rights reserved.
This paper investigates the applicability and effectiveness of modern heuristic techniques for solving SVC placement problem. Specifically, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and evolutionary PS...
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This paper investigates the applicability and effectiveness of modern heuristic techniques for solving SVC placement problem. Specifically, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and evolutionary PSO (EPSO) have been developed and successfully applied to find the optimal placement of SVC devices. The main objective of the proposed problem is to find the optimal number and sizes of the SVC devices to be installed in order to enhance the load margin when contingencies happen. SVC installation cost and load margin deviation are subject to be minimized. The proposed approaches have been successfully tested on IEEE 14 and 57 buses systems and a comparative study is illustrated. To evaluate the capability of the proposed techniques to solve large scale problems, they are also applied to a large scale mixed-integer nonlinear reactive power planning problem. Results of the application to IEEE 14 bus test system prove the feasibility of the proposed approaches and outperformance of PSO based techniques over GA.
This paper presents two new computationally efficient improved stochastic algorithms for solving Security Constrained Optimal Power Flow (SCOPF) in interconnected power systems. These algorithms are based on the combi...
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This paper presents two new computationally efficient improved stochastic algorithms for solving Security Constrained Optimal Power Flow (SCOPF) in interconnected power systems. These algorithms are based on the combined application of Fuzzy Logic strategy incorporated in both evolutionary programming (EP) and Tabu Search (TS) algorithms, hence named as Fuzzy Mutated evolutionary programming (FMEP) and Fuzzy Guided Tabu Search (FGTS). The SCOPF calculation determines the schedule power system controls to achieve operation at a desired security level, while minimizing the generator fuel cost. The proposed methods are tested on single area IEEE 30-bus system and interconnected two area systems. The optimal solutions obtained using EP, TS, FMEP and FGTS are compared and analyzed. The analysis reveals that the proposed algorithms are relatively simple, efficient, reliable and suitable for real-time applications. And these algorithms can provide accurate solution with fast convergence and have the potential to be applied to other power engineering problems.
Artificial Neural Networks (ANNs) have been applied to a variety of classification and learning tasks. The use of evolutionary Algorithms (EA) as one of the fastest, robust and efficient global search techniques has a...
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ISBN:
(纸本)9780769537054
Artificial Neural Networks (ANNs) have been applied to a variety of classification and learning tasks. The use of evolutionary Algorithms (EA) as one of the fastest, robust and efficient global search techniques has allowed different properties of artificial neural networks to be evolved. This paper proposes the possibility of using differential evolution for Determining an ANN Architecture (DNNA). We explain how to use differential evolution's application for determining an ANN architecture. The approach we describe is innovative and has only been successfully applied and implemented for the first time, although the idea of Differential Evolution has been applied in various fields since the last decade. In this work, we proposed an algorithm based on Differential Evolution that uses a minimum number of user specified parameters in determining an ANN architecture. By using back-propagation algorithm to train the ANN architecture partially during the evolution process, DNNA is evaluated on five benchmark classification problems, namely, Cancer, Diabetes, Heart Disease, Thyroid, and the Australian Credit Card problem. Through performance analysis and simulation studies, we show that DNNA can produce ANN architecture with good generalization abilities, but with less number of training cycles when compared with an evolutionary programming approach and standard back-propagation.
This article provides a brief overview of the field of evolutionary Computation. It describes the important historical developments that shaped the field. It summarizes the field as it exists today and discusses some ...
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This article provides a brief overview of the field of evolutionary Computation. It describes the important historical developments that shaped the field. It summarizes the field as it exists today and discusses some of the important directions in which the field is developing. (C) 2009 John Wiley & Sons, Inc.
In this paper, we present exponential evolutionary programming (EEP) with the mutation based on double exponential probability distribution. By controlling the parameter value of EEP, an effective convergence of evolu...
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ISBN:
(纸本)0889865361
In this paper, we present exponential evolutionary programming (EEP) with the mutation based on double exponential probability distribution. By controlling the parameter value of EEP, an effective convergence of evolutionary programming (EP) can be expected comparing with conventional algorithm. Moreover, a new EEP without strategy parameter is proposed and shown that its performance is superior to other EP algorithm with simple parameter setting.
Based on biological immune theory, a new immune algorithm is presented. Compared with the classical evolutionary programming and evolutionary algorithms with chaotic mutations, experimental results show that the propo...
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ISBN:
(纸本)9781424447947
Based on biological immune theory, a new immune algorithm is presented. Compared with the classical evolutionary programming and evolutionary algorithms with chaotic mutations, experimental results show that the proposed algorithm, parallel chaos immune evolutionary programming, is of high efficiency and can effectively prevent premature convergence. A three-layer feed-forward neural network is designed to predict the state of charge (SOC) of Ni-MH batteries. Initially, partial least square regression is used to select input variables. Then, five variables, battery terminal voltage, voltage derivative, voltage second derivative, discharge current and battery temperature, are selected as the inputs of NN. In order to overcome the weakness of BP algorithm, the proposed algorithm is adopted to train weights. Finally, under the state of dynamic power cycle, the estimated SOC from NN model and the measured SOC from experiments are compared, and the results conform that the proposed approach can provide an accurate estimation of the SOC.
Although the benefits of forming cells are widely recognized, certain industrial decision makers do not wish an entire transformation because of possible changes that may occur, for example, in the demand, mix or rout...
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Although the benefits of forming cells are widely recognized, certain industrial decision makers do not wish an entire transformation because of possible changes that may occur, for example, in the demand, mix or routing of products. Therefore, hybrid organization of functional departments and manufacturing cells in the same shop may appear quite relevant. The problem addressed here is how to allocate machines and products so as to design such systems that take advantages of both types of layout. We suggest an approach that favours cells for the parts that are regularly manufactured over time (i.e. stable parts), and functional departments for the parts that require more flexibility. We also consider the difficulties arising in the collection of precise data in reality. Hybrid shop design problem is formulated as a constrained fuzzy multi-objective optimization problem and an evolutionary programming algorithm is devised to solve it. The approach is illustrated in test examples.
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