evolutionary programming incorporating neural network (EPNN) is proposed to obtain the solution of Transient Stability Constrained Optimal Power Flow (TSCOPF) in this paper. evolutionary programming (EP) is selected a...
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ISBN:
(纸本)9781424417636
evolutionary programming incorporating neural network (EPNN) is proposed to obtain the solution of Transient Stability Constrained Optimal Power Flow (TSCOPF) in this paper. evolutionary programming (EP) is selected as a main optimizer while the neural network is a supplementary tool to enhance the computational speed by screening out individuals, which have very high or low degrees of stability. Swing equation and limit of rotor angle deviation with respect to centre of inertia (COI) are treated as additional constraints in transient stability concern. The generator fuel cost minimization is selected as the objective function for TSCOPF. The proposed method is tested on IEEE 30-bus system with three different generator cost curves to account for the combined cycle generating unit and valve point loading effect of a thermal generating unit. A three-phase fault at a specific transmission line is considered as a single contingency. The simulation results show that the proposed method is capable of searching for the optimal or near optimal solution of TSCOPF. Moreover, the exploitation of the neural network leads to huge computational time saving due to absence of time-domain simulation for some individuals during EP search.
evolutionary programming is an important kind of evolutionary computation method, which is very efficient especially for continuous parameter optimization problems. Several different versions of evolutionary programmi...
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ISBN:
(纸本)1424403316
evolutionary programming is an important kind of evolutionary computation method, which is very efficient especially for continuous parameter optimization problems. Several different versions of evolutionary programming have been proposed during recent years. This paper gives a detailed review of the research status aiming at better further research.
In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary ...
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In this paper, an evolutionary approach to solve the mobile robot path planning problem is proposed. The proposed approach combines the artificial bee colony algorithm as a local search procedure and the evolutionary programming algorithm to refine the feasible path found by a set of local procedures. The proposed method is compared to a classical probabilistic roadmap method (PRM) with respect to their planning performances on a set of benchmark problems and it exhibits a better performance. Criteria used to measure planning effectiveness include the path length, the smoothness of planned paths, the computation time and the success rate in planning. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed method are also shown. (C) 2015 Elsevier B.V. All rights reserved.
This paper studies evolutionary programming and adopts reinforcement learning theory to learn individual mutation operators. A novel algorithm named RLEP (evolutionary programming based on Reinforcement Learning) is p...
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This paper studies evolutionary programming and adopts reinforcement learning theory to learn individual mutation operators. A novel algorithm named RLEP (evolutionary programming based on Reinforcement Learning) is proposed. In this algorithm, each individual learns its optimal mutation operator based on the immediate and delayed performance of mutation operators. Mutation operator selection is mapped into a reinforcement learning problem. Reinforcement learning methods are used to learn optimal policies by maximizing the accumulated rewards. According to the calculated Q function value of each candidate mutation operator, an optimal mutation operator can be selected to maximize the learned Q function value. Four different mutation operators have been employed as the basic candidate operators in RLEP and one is selected for each individual in different generations. Our simulation shows the performance of RLEP is the same as or better than the best of the four basic mutation operators. (c) 2007 Elsevier Inc. All rights reserved.
The authors present a novel evolutionary programming (EP) based algorithm for the short-term hydrothermal scheduling problem. To more realistically represent the relationship between the generation and amount of water...
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The authors present a novel evolutionary programming (EP) based algorithm for the short-term hydrothermal scheduling problem. To more realistically represent the relationship between the generation and amount of water discharge for hydroaggregates, the generation models of the hydro plants as well as thermal plants are often expressed as nonlinear and nonsmooth curves with prohibited areas. The advantage of the proposed algorithm is that it is capable of determining the global or near global optimal solutions to such an optimisation problem with multiple local minima. The developed algorithm is illustrated and tested on two model systems. The test results are compared with those obtained using gradient search, simulated annealing and genetic algorithm methods. Numerical results show that the proposed EP-based algorithm can provide accurate solutions within a reasonable time.
The optimization of bioreactor operations towards swainsonine production was performed using an artificial neural network coupled evolutionary program (EP)-based optimization algorithm fitted with experimental one-fac...
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The optimization of bioreactor operations towards swainsonine production was performed using an artificial neural network coupled evolutionary program (EP)-based optimization algorithm fitted with experimental one-factor-at-a-time (OFAT) results. The effects of varying agitation (300-500 rpm) and aeration (0.5-2.0 vvm) rates for different incubation hours (72-108 h) were evaluated in bench top bioreactor. Prominent scale-up parameters, gassed power per unit volume (P (g)/V (L), W/m(3)) and volumetric oxygen mass transfer coefficient (K (L) a, s(-1)) were correlated with optimized conditions. A maximum of 6.59 +/- A 0.10 mu g/mL of swainsonine production was observed at 400 rpm-1.5 vvm at 84 h in OFAT experiments with corresponding P (g)/V-L and K (L) a values of 91.66 W/m(3) and 341.48 x 10(-4) s(-1), respectively. The EP optimization algorithm predicted a maximum of 10.08 mu g/mL of swainsonine at 325.47 rpm, 1.99 vvm and 80.75 h against the experimental production of 7.93 +/- A 0.52 mu g/mL at constant K (L) a (349.25 x 10(-4) s(-1)) and significantly reduced P (g)/V (L) (33.33 W/m(3)) drawn by the impellers.
This paper presents an algorithm, for solving security constrained economic dispatch (SCED) problem, through the application of evolutionary programming (EP). The controllable system quantities in the base case state ...
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This paper presents an algorithm, for solving security constrained economic dispatch (SCED) problem, through the application of evolutionary programming (EP). The controllable system quantities in the base case state are optimized, to minimize some defined objective function, subject to the base case operating constraints as well as the contingency case security constraints. Two representative systems: 10-bus [10] and adapted IEEE 30-bus [20] systems are taken for investigations. The SCED results obtained using EP are compared, with those obtained using quadratic programming [Fan JY, Zhang L. Real-time economic dispatch withline flow and emission constrains using quadratic programming. IEEE Trans Power Syst 1998;13(2):320-5] and successive linear programming [Kuppusamy K. Successive liner programming methods for security-related optimization in power systems. India:PhD Thesis;1981]. The investigations reveal that, the proposed algorithm is relatively simple, reliable, efficient and suitable for on-line applications. (c) 2005 Elsevier Ltd. All rights reserved.
This article presents evolutionary programming based on interactive fuzzy satisfying method for multiobjective generation scheduling of fixed head hydro plants and thermal plants with nonsmooth fuel cost and emission ...
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This article presents evolutionary programming based on interactive fuzzy satisfying method for multiobjective generation scheduling of fixed head hydro plants and thermal plants with nonsmooth fuel cost and emission level functions. The multiobjective problem is formulated considering two objectives;(1) economy, and (2) emission. These two objectives are mutually conflicting and equally important. Assuming that the decision maker (DM) has imprecise or fuzzy goals for each of the objective functions, the multiobjective problem is transformed into a minimax problem, which is then handled by the evolutionary programming technique. The solution methodology can offer a global or near-global noninferior solution for the DM. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared to those found by interactive fuzzy satisfying method based on simulated annealing technique.
In this paper, a fuzzy clustering method based on evolutionary programming (EPFCM) is proposed. The algorithm benefits from the global search strategy of evolutionary programming, to improve fuzzy c-means algorithm (F...
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In this paper, a fuzzy clustering method based on evolutionary programming (EPFCM) is proposed. The algorithm benefits from the global search strategy of evolutionary programming, to improve fuzzy c-means algorithm (FCM). The cluster validity can be measured by some cluster validity indices. To increase the convergence speed of the algorithm, we exploit the modified algorithm to change the number of cluster centers dynamically. Experiments demonstrate EPFCM can find the proper number of clusters, and the result of clustering does not depend critically on the choice of the initial cluster centers. The probability of trapping into the local optima will be very lower than FCM. (C) 2009 Elsevier Ltd. All rights reserved.
In this letter an evolutionary programming (EP) algorithm adapting a new mutation operator is presented. Unlike most previous EPs, in which each individual is mutated on its own, each individual in the proposed algori...
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In this letter an evolutionary programming (EP) algorithm adapting a new mutation operator is presented. Unlike most previous EPs, in which each individual is mutated on its own, each individual in the proposed algorithm is mutated in cooperation with the other individuals. This not only enhances convergence speed but also gives more chance to escape from local minima. (C) 2002 Elsevier Science B.V. All rights reserved.
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