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.
Decentralized control is a practical control methodology for large-scale multivariable systems. This paper presents a LQR design methodology to design a state-feedback decentralized high-gain analog controller, which ...
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Decentralized control is a practical control methodology for large-scale multivariable systems. This paper presents a LQR design methodology to design a state-feedback decentralized high-gain analog controller, which gives the desired decentralized performance of the controlled analog system. Then, a prediction-based decentralized low-gain digital controller is developed from the decentralized high-gain analog controller for the hybrid controlled system. As a result, the complexity and cost of hardware implementation of the controller can be significantly reduced. In order to improve the performance of the decentralized hybrid system, the evolutionary programming (EP) is employed to tune the observer-based decentralized tracker. Some examples are presented to illustrate the developed design methodology.
In evolutionary programming, each parent has two pieces of information: location and cost. The cost of parent specifies whether its location is suitable for breeding offspring or not. If the parent's cost is an ac...
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In evolutionary programming, each parent has two pieces of information: location and cost. The cost of parent specifies whether its location is suitable for breeding offspring or not. If the parent's cost is an acceptable value, producing offspring (or even steering other offspring) in the parent's area is advisable. This information is used in estimating the region of global minimum;then, using the state feedback controller, the offspring is steered to the optimal region. In the proposed method, the cost and coordination of parents have been used for breeding more elite individuals. Many (sixty-five) well-known cost functions have been selected from different references to reveal the pros and cons of our algorithms. In the first stage, the proposed algorithm has been compared inside the EP family. This stage shows promising results for the proposed algorithm. In the second stage, comparison has been performed out of the EP frontiers in which algorithms are state-of-the-art in the optimization field, and are well known inside and outside of their own families. The statistic test has been performed among the algorithms. CPU time and its sensitivity to variable bounds, population and cost function dimensions have been studied. Finally, the proposed method is used in designing the nonlinear minimum variance controller for CSTR (Continuous Stirred-Tank Reactor) benchmark system.
Applications in evolutionary programming have suggested the use of further stable probability distributions, such as Cauchy and Levy, in the random process associated with the mutations, as an alternative to the tradi...
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Applications in evolutionary programming have suggested the use of further stable probability distributions, such as Cauchy and Levy, in the random process associated with the mutations, as an alternative to the traditional, also stable, normal distribution. This work goes further along the encouraging results of the latter, by extending them in a self-adaptive way, with algorithms that are in tune with the standard lineage of evolutionary programming. Evaluations that rely upon standard analytical bench-marking functions and comparative performance tests between them were carried out in respect to the baseline defined by the standard evolutionary programming algorithm that relies on normal distribution. Additional comparative studies were made in respect to various self-adaptive approaches, also proposed herein, and a method drawn from the literature. The results lead to numerical and statistical superiority of the more general stable distribution based approach, when compared with the baseline, and is unclear in regard to the method drawn from the literature, possibly due to distinct implementation details.
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.
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 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.
The present work evaluates the use of the evolutionary programming in problems of Industrial Automation. It is well known that technical of the evolutionary calculation as the Genetic Algorithms have provided satisfac...
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The present work evaluates the use of the evolutionary programming in problems of Industrial Automation. It is well known that technical of the evolutionary calculation as the Genetic Algorithms have provided satisfactory results when facing problems of automatic control, especially of continuous type, while the study of discrete dynamics have been relegated. In this work, an identification technique based on the evolutionary programming is proposed for Discrete-Event Dynamic Systems (DEDS), using finite state machines (like the machines of Mealy).
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