This paper proposes an improved evolutionary programming (IEP) and its hybrid version combined with the nonlinear interior point (IP) technique to solve the optimal reactive power dispatch (ORPD) problems. In an IEP m...
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This paper proposes an improved evolutionary programming (IEP) and its hybrid version combined with the nonlinear interior point (IP) technique to solve the optimal reactive power dispatch (ORPD) problems. In an IEP method, the common practices in regulating reactive power are followed in adjusting the mutation direction of control variables in order to increase the possibility of keeping state variables within bounds. The IEP is also hybridized with the IP method to obtain a fast initial solution, which is then used as a highly evolved individual in the initial population of the improved EP method. Numerical tests of the proposed algorithm on the IEEE 118-bus system and a realistic power system in Western China are very encouraging compared with the existing ORPD algorithms in term of computational efficiency and optimality.
Photomosaic images are a type of images consisting of various tiny images. In the past, many approaches have been proposed trying to automatically compose photomosaic images. To obtain a better visual sense and satisf...
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Photomosaic images are a type of images consisting of various tiny images. In the past, many approaches have been proposed trying to automatically compose photomosaic images. To obtain a better visual sense and satisfy some commercial requirements, a constraint that a tiny image should not be repeatedly used many times is usually added. With the constraint, algorithms using greedy mechanism fail to solve it. In this paper, we present an approach called clustering based evolutionary programming to deal with the problem. Our new approach has a similar mechanism to that of evolutionary programming (we adopt mutation, selection and fitness evaluation) and uses the normalized color histogram information as the prior information. The two characteristics make our approach converges fast and performs well. In our experiment, the proposed algorithm is compared with the state of the art algorithms and software. The results indicate that our algorithm is able to generate higher quality photomosaic images.
This paper describes the use of evolutionary programming (EP) integrated with a simulation model of a manufacturing system to determine the minimum number of kanbans and corresponding production trigger values require...
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This paper describes the use of evolutionary programming (EP) integrated with a simulation model of a manufacturing system to determine the minimum number of kanbans and corresponding production trigger values required to meet demand. For this problem, a new two step heuristic is developed based on EP and the classical kanban sizing equation used by Toyota. The procedure is illustrated with an applied problem and the results indicate that the new heuristic provides good solutions to the problem.
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.
The maintenance scheduling problem has several uncertainties associated with it. This paper presents a fuzzy model for the integrated generation and transmission maintenance scheduling problem (MS) that accounts for s...
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The maintenance scheduling problem has several uncertainties associated with it. This paper presents a fuzzy model for the integrated generation and transmission maintenance scheduling problem (MS) that accounts for such uncertainties, and introduces a solution technique to solve for the optimal schedule. This technique is based on-evolutionary programming (EP) to find a near-optimal solution and the Hill-Climbing (HCT) method to maintain feasibility during the solution process. It also uses a, fuzzy comparison technique developed by the authors to compare individuals. The proposed technique generates fuzzy ranges for the maintenance and production costs that reflect the problem uncertainties. The paper includes test results on the IEEE 118-bus system with 33 generating units and 179 transmission lines. Results are encouraging and indicate the viability of the proposed technique.
The particle swarm optimization (PSO) algorithm and two variants of the evolutionary programming (EP) are applied to the several function optimization problems and the conformation optimization of atomic clusters to c...
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The particle swarm optimization (PSO) algorithm and two variants of the evolutionary programming (EP) are applied to the several function optimization problems and the conformation optimization of atomic clusters to check the performance of these algorithms as a general-purpose optimizer. It was found that the PSO is superior to the EP though the PSO is not equipped with the mechanism of self-adaptation of search strategies of the EP. The PSO cannot find the global minimum for the atomic cluster but can find it for similar multi-modal benchmark functions of the same size. The size of the cluster which can be handled by the PSO and the EP is limited, and is similar to the one amenable to the popular simulated annealing. The result for benchmark functions only serves as an indication of the performance of the algorithm.
The optimal/shortest path planning is one of the fundamental needs for efficient operation ofmobile robot. This research article explores the application of artificial bee colony (ABC) algorithm and evolutionary progr...
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The optimal/shortest path planning is one of the fundamental needs for efficient operation ofmobile robot. This research article explores the application of artificial bee colony (ABC) algorithm and evolutionary programming (EP) optimization algorithm to resolve the problem of path planning in an unknown or partially known environment. The ABC algorithm is used for native ferreting procedure and EP for refinement of achieved feasible path. Conventional path planning methods based on ABC-EP didn't consider the distance between newbee position and nearby obstacles for finding the optimal path, which in turn increases the path length, path planning time, or search cost. To overcome these issues, a novel strategy based on improved ABC-EP has been proposed. The improved ABC-EP finds the optimum path towards the goal position and gets rid of obstacles without any collision using food points which are randomly distributed in the environment. The criteria on which it selects the best food point (V-best) not only depend upon the shortest distance of that food point to the goal position but also depend upon the distance of that food point from the nearest obstacles. A number of comparative analyses have been performed in simulation scenario to verify improved ABC-EP's performance and efficiency. The results demonstrate that proposed improved ABC-EP performs better and more effectively as compared to conventional ABC-EP with the improvement of 5.75% in path length, 44.38% in search cost, and 41.08% in path smoothness. The improved ABC-EP achieved optimum path with shortest path length in less time.
Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an effective method ba...
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Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an effective method based on evolutionary programming (EP) and Genetic Algorithm (GA) to identify the switching operation plan for feeder reconfiguration and distributed generation size simultaneously. The main objectives of this paper are to gain the lowest reading of real power losses, upgrade the voltage profile in the system as well as satisfying other operating constraints. Their impacts on the network real power losses and voltage profiles are investigated. A comprehensive performance analysis is carried out on IEEE 33-bus radial distribution systems to prove the efficiency of the proposed methodology. The test result on the system showed the power loss reduction, and voltage profile improvement of the EP is superior to the GA method.
evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro...
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evolutionary computations are very effective at performing global search (in probability), however, the speed of convergence could be slow. This paper presents an evolutionary programming algorithm combined with macro-mutation (MM), local linear bisection search (LBS) and crossover operators for global optimization. The MM operator is designed to explore the whole search space and the LBS operator to exploit the neighborhood of the solution. Simulated annealing is adopted to prevent premature convergence. The performance of the proposed algorithm is assessed by numerical experiments on 12 benchmark problems. Combined with MM, the effectiveness of various local search operators is also studied. (c) 2004 Elsevier B.V. All rights reserved.
The paper proposes an application of evolutionary programming (EP) to fault-section estimation in power systems. Several techniques have been employed to solve this problem so far. A genetic algorithm (GA) has been re...
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The paper proposes an application of evolutionary programming (EP) to fault-section estimation in power systems. Several techniques have been employed to solve this problem so far. A genetic algorithm (GA) has been reported to be one of these techniques. In order to measure the efficiency of EP and make comparisons, a GA has also been used to solve the same problem. Different parameters which affect the EP convergence have been investigated. Two object-oriented software codes have been developed to implement the algorithms. A sample power system is used to examine the algorithms. It shows that EP is superior to the GA for the type of coding strategy and evolution as defined for the GA.
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