A novel evolutionary programming algorithm, which not only has a rapid convergence rate but also maintains the diversity of the population so as to escape from local optima, is proposed in this paper. In addition, a m...
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A novel evolutionary programming algorithm, which not only has a rapid convergence rate but also maintains the diversity of the population so as to escape from local optima, is proposed in this paper. In addition, a multi-modal test function is presented and is used to indicate the efficiency of this algorithm. Several application examples are given to show its usefulness. (C) 1999 Elsevier Science Ltd. All rights reserved.
This paper proposes an improved evolutionary programming (EP) method with deterministic mutation factor for on line PID parameters optimization of hydro-turbine governing systems. The mutation factors are usually gene...
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This paper proposes an improved evolutionary programming (EP) method with deterministic mutation factor for on line PID parameters optimization of hydro-turbine governing systems. The mutation factors are usually generated with Gaussian or Cauchy random series in conventional evolutionary programming algorithms. Considering the difficulties of on line optimal parameters settings resulting from nonlinear time-variant hydro-turbine governing systems, this paper introduces deterministic chaos dynamics into the mutation operation of EP and provides a deterministic chaotic mutation evolutionary programming (DCMEP) method. The improved method develops the traditional concept that implements mutation operation with a fixed random distribution using a quasi-random deterministic way to generate the mutation factor. The test result of the two real hydro-turbine governing systems shows that the improved method can optimize the PID parameters efficiently, and the system has the characteristics of stability;low overshoot level and fast response. (c) 2005 Elsevier Ltd. All rights reserved.
Network reconfiguration for loss reduction in distribution systems is a very important way to save energy. However, due to its nature it is an inherently difficult optimisation problem. A new type of evolutionary sear...
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Network reconfiguration for loss reduction in distribution systems is a very important way to save energy. However, due to its nature it is an inherently difficult optimisation problem. A new type of evolutionary search technique, evolutionary programming (EP), has been adopted and improved for this particular application. To improve the performance of EP, a fuzzy controlled EP (FCEP), based on heuristic information, is first proposed. The mutation fuzzy controller adaptively adjusts the mutation rate during the simulated evolutionary process. The status of each switch in distribution systems is naturally represented by a binary control parameter 0 or 1. The length of string is much shorter than those proposed by others. A chain-table and combined depth-first and breadth-first search strategy is employed to further speed up the optimisation process. The equality and inequality constraints are imbedded into the fitness function by penalty factors which guarantee the optimal solutions searched by the FCEP are feasible. The implementation of the proposed FCEP for feeder reconfiguration is described in detail. Numerical results are presented to illustrate the feasibility of the proposed FCEP.
In this paper, a new bidding strategy for a day-ahead market is formulated. The proposed algorithm is developed from the viewpoint of a generation company wishing to maximize a profit as a participant in the deregulat...
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In this paper, a new bidding strategy for a day-ahead market is formulated. The proposed algorithm is developed from the viewpoint of a generation company wishing to maximize a profit as a participant in the deregulated power and reserve markets. Separate power and reserve markets are considered, both are operated by clearing price auction system. The optimal bidding parameters for both markets are determined by solving an optimization problem that takes unit commitment constraints such as generating limits and unit minimum up/down time constraints into account. This is a non-convex and non-differentiable which is difficult to solve by traditional optimization techniques. In this paper, evolutionary programming is used to solve the problem. The algorithm is applied to both single-sided and double-sided auctions, numerical simulations are carried out to demonstrate the performance of the proposed scheme compared with those obtained from a sequential quadratic programming. (C) 2005 Elsevier Ltd. All rights reserved.
A traditional mathematical model for maintenance scheduling of power generation systems may give an optimal schedule for a power system with known conditions. A change of the system condition due to uncertainties or s...
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A traditional mathematical model for maintenance scheduling of power generation systems may give an optimal schedule for a power system with known conditions. A change of the system condition due to uncertainties or sudden changes may tender the resulting optimal schedule unsuitable or inapplicable for the power system under study. This paper presents a fuzzy model and an evolutionary programming-based solution technique for the security-constrained maintenance scheduling (MS) problem of generation systems with uncertainties in the load and fuel and maintenance costs. The proposed technique results are fuzzy optimal cost range that reflects the problem uncertainties. The technique solves a decomposed maintenance model of two interrelated subproblems, namely the maintenance and the security-constrained economic dispatch problem. Test results on the IEEE 30-bus system with six generating units reported in this paper are quite encouraging. (C) 2003 Elsevier Science B.V. All rights reserved.
This paper studies the economical operation of cogeneration systems under emission control with NOx and SOx from fossil-fueled thermal generation. The emission model is formulated as a function of fuel enthalpy depend...
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This paper studies the economical operation of cogeneration systems under emission control with NOx and SOx from fossil-fueled thermal generation. The emission model is formulated as a function of fuel enthalpy dependent on the emission factor. The objective function includes fuel cost, emission cost, and tie-line energy cost, subject to the use of mixed fuels, operational limits, and emission constraints. An evolutionary programming was adopted to solve this problem. The steams, fuel mix, and generations will be found by considering the time-of-use dispatch between cogeneration systems and utility companies. A real cogeneration system is given to illustrate the proposed method. (C) 2001 Elsevier Science Ltd. All rights reserved.
In this paper, the observer-based iterative learning control with/without evolutionary programming algorithm is proposed for MIMO nonlinear systems. While the learning gain involves some immeasurable states, this pape...
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In this paper, the observer-based iterative learning control with/without evolutionary programming algorithm is proposed for MIMO nonlinear systems. While the learning gain involves some immeasurable states, this paper proposes the observer-based iterative learning control (ILC) for nonlinear systems and guarantees the tracking error convergences to zero via continual learning. Moreover, a sufficient condition has been presented to alleviate the traditional constraint, i.e., identical initial state, in the convergence analysis. Then, an idea of feasible reference based on polynomial approximation is proposed to overcome the limitation of ILC - initial state error. To speed up the convergence of the iterative learning control, evolutionary programming is applied to search for the optimal and feasible learning gain to reduce the training aim. In addition, two improved issues of ILC, an appropriate selection of the initial control input and the improved learning rule for the system whose product matrix of output matrix C and input matrix B is not full rank, are presented in this paper. Three multi-input multi-output (MIMO) illustrative examples are presented to demonstrate the effectiveness of the proposed methodology.
Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing opera...
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Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing operations. To model consumer responses to direct marketing, this study proposes Bayesian networks learned by evolutionary programming. Using a large direct marketing data set, we tested the endogeneity bias in the recency, frequency, monetary value (RFM) variables using the control function approach;compared the results of Bayesian networks with those of neural networks, classification and regression tree (CART), and latent class regression;and applied a tenfold cross-validation. The results suggest that Bayesian networks have distinct advantages over the other methods in accuracy of prediction, transparency of procedures, interpretability of results, and explanatory insight. Our findings lend strong support to Bayesian networks as a robust tool for modeling consumer response and other marketing problems and for assisting management decision making.
This paper presents an evolutionary programming (EP)-based technique to the unified model of the maintenance scheduling (MS) problem of power generation and transmission systems. In this paper, the Hill-Climbing techn...
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This paper presents an evolutionary programming (EP)-based technique to the unified model of the maintenance scheduling (MS) problem of power generation and transmission systems. In this paper, the Hill-Climbing technique (HCT) is used in conjunction with the EP to find a feasible solution in the neighborhood of the new infeasible solutions during the solution process. The EP search ability and the feasibility watch of the HCT motivate the sequential solution of the two interrelated subproblems of the MS problem. The paper reports test results of the proposed algorithm on the IEEE 30-bus system with six generating units and 41 transmission lines. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper proposes a multitime-interval scheduling for the daily operation of a two-cogeneration system connected with auxiliary devices, which include auxiliary boilers, heat stop-age tanks, electricity chargers and...
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This paper proposes a multitime-interval scheduling for the daily operation of a two-cogeneration system connected with auxiliary devices, which include auxiliary boilers, heat stop-age tanks, electricity chargers and independent generators. The efficiency of a cogeneration system depends on the production of thermal and electrical energy which is modelled with a quadratic equation obtained by the least-squares method. evolutionary programming (EP) is used to establish operation scheduling for the cogeneration system. For this complex scheduling problem with multi-variables in multitime-intervals, the optimal operational cost for the cogeneration system obtained with EP is much lower than the initial feasible solution obtained by trial and error. (C) 1998 Elsevier Science Ltd. All rights reserved.
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