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JPEG 2000 wavelet filter design framework with chaos evolutionary programming

SIGNAL PROCESSING 2008年第10期88卷 2542-2553页

作者： Guo, Shu-Mei Chang, Wen-Hsin Tsai, Jason S-H Zhuang, Bo-Liang Chen, Li-Chun Natl Cheng Kung Univ Control Syst Lab Dept Elect Engn Tainan 701 Taiwan Natl Cheng Kung Univ Dept Comp & Informat Engn Tainan 701 Taiwan

JPEG 2000 is an international standard for still image compression, which is based on wavelet transformation and the Daubechies 9/7 filter adopted for lossy compression. Considering that a wavelet filter might be suitable for one image but not for the other in regard to compression quality, in this paper, we propose a novel filter design framework based on the Daubechies 9/7 filter, which employs chaos evolution programming (CEP) to optimize the wavelet filter for both the universal images and each specific image, respectively. The customized filter design is ready to incorporate into the JPEG 2000 compression/decompression since the filter coefficients can be constructed by only one tuning parameter, which can be easily packaged into the modified JPEG 2000 header. Experimental results show that CEP-trained filters achieve higher image quality. (C) 2008 Elsevier B.V. All rights reserved.

关键词： image coding JPEG 2000 discrete wavelet transformation chaos evolutionary programming

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Identification of ARMAX model for short term load forecasting: An evolutionary programming approach

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IEEE TRANSACTIONS ON POWER SYSTEMS 1996年第1期11卷 403-408页

作者： Yang, HT Huang, CM Huang, CL NATL CHENG KUNG UNIV DEPT ELECT ENGNTAINAN 701TAIWAN

This paper proposes a new evolutionary programming (EP) approach to identify the autoregressive moving average with exogenous variable (ARMAX) model for one day to one week ahead hourly load demand forecasts. Typically, the surface of forecasting error function possesses multiple local minimum points. Solutions of the traditional gradient search based identification technique therefore may stall at the local optimal points which lead to an inadequate model. By simulating natural evolutionary process, the EP algorithm offers the capability of converging towards the global extremum of a complex error surface. The developed EP based load forecasting algorithm is verified by using different types of data for practical Taiwan Power (Taipower) system and substation load as well as temperature values. Numerical results indicate the proposed EP approach provides a method to simultaneously estimate the appropriate order and parameter values of the ARMAX model for diverse types of load data. Comparisons of forecasting errors are made to the traditional identification techniques.

关键词： model short term load forecasting evolutionary programming parameter estimation

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Running-time analysis of evolutionary programming based on Lebesgue measure of searching space

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NEURAL COMPUTING & APPLICATIONS 2018年第2期30卷 617-626页

作者： Zhang, Yushan Huang, Han Lin, Zhiyong Hao, Zhifeng Hu, Guiwu Guangdong Univ Finance & Econ Sch Math & Stat Guangzhou 510320 Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Polytech Normal Univ Dept Comp Sci Guangzhou 510665 Guangdong Peoples R China Foshan Univ Sch Math & Big Data Foshan 528000 Peoples R China

There have been many studies on the runtime analysis of evolutionary algorithms in discrete optimization, and however, relatively few homologous results have been obtained on continuous optimization, such as evolutionary programming (EP). This paper presents an analysis of the running time (as approximated by the mean first hitting time) of two EP algorithms based on Gaussian and Cauchy mutations, using an absorbing Markov process model. Given a constant variation, we analyze the running-time upper bound of special Gaussian mutation EP and Cauchy mutation EP, respectively. Our analysis shows that the upper bounds are impacted by individual number, problem dimension number, searching range, and the Lebesgue measure of the optimal neighborhood. Furthermore, we provide conditions whereby the mean running time of the considered EPs can be no more than a polynomial of n. The condition is that the Lebesgue measure of the optimal neighborhood is larger than a combinatorial computation of an exponential and the given polynomial of n. In the end, we present a case study on sphere function, and the experiment validates the theoretical result in the case study.

关键词： evolutionary algorithm Running-time analysis evolutionary programming Lebesgue measure Optimal epsilon neighborhood

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A Single Component Mutation evolutionary programming

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APPLIED MATHEMATICS AND COMPUTATION 2010年第10期215卷 3759-3768页

作者： Ji, Mingjun Yang, Hualong Yang, Yongzhi Jin, Zhihong Dalian Maritime Univ Transportat Management Coll Dalian 116026 Peoples R China

In this paper, a novel evolutionary programming is proposed for solving the upper and lower bound optimization problems as well as the linear constrained optimization problems. There are two characteristics of the algorithm: first, only one component of the current solution is mutated in each iteration;second, it can solve the linear constrained optimization problems directly without converting it into unconstrained problems. By solving two kinds of the optimization problems, the algorithm can not only effectively find optimal or close to optimal solutions but also reduce the number of function evolutions compared with the other heuristic algorithms. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Global optimization Constrained optimization problems Heuristic evolutionary programming Genetic algorithms

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Solving resource-constrained project scheduling problem with evolutionary programming

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2013年第9期64卷 1327-1335页

作者： Sebt, M. H. Alipouri, Y. Alipouri, Y. Amirkabir Univ Technol Tehran 0098 Iran Univ Sci & Technol Tehran Iran

This paper presents an evolutionary programming (EP)-based approach to solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling, with minimization of project duration as the objective subject to precedence and resource constraints. The individual representation of EP for the problem is based on random keys. The serial generation scheme is used in the decoding scheme to generate the project plan. Experimental analyses are presented to investigate the performance of the proposed EP-based methodology, including comparison of the four variants of EP, namely, CEP, FEP, MCEP and IMCEP, with each other and GA to find the best variant of EP for the RCPSP, and comparison of this best variant of EP (MCEP) with other approaches using the J30 standard instances set in PSPLIB. The computational results validate the effectiveness of the proposed algorithm.

关键词： project management resource-constrained project scheduling evolutionary programming random key representation

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Application of Grey Correlation Analysis in evolutionary programming for Distribution System Feeder Reconfiguration

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IEEE TRANSACTIONS ON POWER SYSTEMS 2010年第2期25卷 1126-1133页

作者： Tsai, Men-Shen Hsu, Fu-Yuan Natl Taipei Univ Technol Grad Inst Automat Technol Taipei Taiwan Natl Taipei Univ Technol Inst Automat Technol Taipei Taiwan

Feeder reconfiguration is a common technique that is used by distribution system operators during normal or emergency operational planning. By changing the status of switches on the distribution systems, the feeders can be reconfigured. During a feeder reconfiguration, more than one objective is considered by the distribution system operators. Due to the complexity of the reconfiguration problems, the system operators are looking for assistance from computer program that can provide adequate switching plans to reconfigure the feeders such that the desired goal can be achieved. Thus, the feeder reconfiguration is a type of discrete multi-objective optimization problems. evolutionary programming (EP) technique is a method that can be applied to identify an optimal switching plan for feeder reconfiguration. A fitness function is required in EP for chromosome selection during reproduction process. The fitness function needs to integrate the objectives to provide a measure for each chromosome. Normalizing the objectives is a typical method for multi-objective optimizations such that these objectives are comparable. In this paper, Gray CoRrelation Analysis (GCRA) method is proposed. The proposed method is used to integrate the objectives and provide a relative measure to a particular switching plan associated with a chromosome without any prior knowledge of the system under reconfiguration. Two different distribution systems are used in this paper to demonstrate how the proposed GCRA is applied during the selection process of EP. Several simulations show that the EP can identify the solution more accurately when GCRA is applied than other methods.

关键词： evolutionary programming grey correlation analysis power distribution planning

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Fuel restricted short term economic dispatch using evolutionary programming for utility system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2003年第10期25卷 821-827页

作者： Kumarappan, N Mohan, MR Anna Univ CEG Sch EEE Madras 600025 Tamil Nadu India

The objective of the paper is to minimize the production cost of the thermal power generation. An elegant approach is presented in order to obtain the equivalent cost function of the participating non-fuel restricted units and the Economic Dispatch Calculations (EDC) are carried out along with fuel restricted units. The evolutionary programming (EP) technique is used for real power optimization with fuel restricted units. The optimal solution is obtained neglecting losses. The Fast Decoupled Load Flow (FDLF) analysis is conducted to find the losses by substituting the generation values. Then the loss is participated among all generating units using participation factor method. The load flow is conducted again and the voltage limit violation is checked. The Algorithm is tested on IEEE 6-bus system IEEE 30-bus system and a 66-bus utility system. The results obtained by this new approach are compared with those obtained using classical method. It is observed that the proposed method is more reliable and efficient. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： economic dispatch calculation evolutionary programming fast decoupled load flow fuel restriction participation factor method

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An evolutionary programming based simulated annealing method for solving the unit commitment problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2007年第7期29卷 540-550页

作者： Christober Asir Rajan, C. Mohan, M. R. Pondicherry Engn Coll Dept EEE Pondicherry 605014 India Anna Univ Dept EEE Madras 600025 Tamil Nadu India

This paper presents a new approach to solve the short-term unit commitment problem using an evolutionary programming based simulated annealing method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. evolutionary programming, which happens to be a global optimisation technique for solving unit commitment Problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status ("flat start"). Here the parents are obtained from a pre-defined set of solution's, i.e. each and every solution is adjusted to meet the requirements. Then, a random recommitment is carried out with respect to the unit's minimum down times. And SA improves the status. The best population is selected by evolutionary strategy. The Neyveli Thermal Power Station (NTPS) Unit-II in India demonstrates the effectiveness of the proposed approach;extensive studies have also been performed for different power systems consists of 10, 26, 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like Dynamic programming, Lagrangian Relaxation and Simulated Annealing and Tabu Search in reaching proper unit commitment. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： unit commitment simulated annealing dynamic programming tabu search evolutionary programming

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Optimal design of power system stabilizers using evolutionary programming

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2002年第4期17卷 429-436页

作者： Abido, MA Abdel-Magid, YL Menoufia Univ Dept Elect Engn Shebin El Korn Minufiya Egypt King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

The optimal design of power system stabilizers (PSSs) using evolutionary programming (EP) optimization technique is presented in this paper. The proposed approach employs EP to search for optimal settings of PSS parameters that shift the system eigenvalues associated with the electromechanical modes to the left in the s-plane. Incorporation of EP algorithm in the design of PSSs significantly reduces the computational burden. The performance of the proposed PSSs under different disturbances, loading conditions, and system configurations is investigated for a multimachine power system. The eigenvalue analysis and the nonlinear simulation results show the effectiveness and robustness of the proposed PSSs to damp out the-local as well as the interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations.

关键词： dynamic stability evolutionary programming PSS design

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Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

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SOFT COMPUTING 2007年第9期11卷 839-846页

作者： Cheong, France Lai, Richard RMIT Univ Melbourne Sch Business IT Melbourne Vic 3000 Australia La Trobe Univ Dept Comp Sci & Comp Engn Bundoora Vic 3083 Australia

With the availability of a wide range of evolutionary Algorithms such as Genetic Algorithms, evolutionary programming, evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.

关键词： evolutionary algorithms evolutionary programming fuzzy logic controller fuzzy systems

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