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International Conference on Power System Technology (POWERCON)

作者： V. S. Pappala I. Erlich S. N. Singh Institute of electrical power systems Universitat Duisburg-Essen Germany Indian Institute of Technology Kanpur India

This paper addresses a multistage stochastic model for the optimal operation of wind farm, pumped storage and thermal power plants. The output of the wind farm and the electrical demand are considered as two independent stochastic processes. The evolution of these processes over time is modeled as a scenario tree. Considering all possible realizations of stochastic process, leads to a huge set of scenarios. These scenarios are reduced by a particle swarm optimization based scenario reduction algorithm. The scenario tree modeling transforms the cost model to a stochastic model. The stochastic model can be used to estimate the operation costs of the hybrid system under the influence of the uncertainties. The stochastic model is solved using adaptive particle swarm optimization.

关键词： Economic Model evolutionary programming Multi-stage Scenario tree Particle Swarm Optimization Random Process Stochastic programming

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Novel integrated optimization algorithm for trajectory planning of robot manipulators based on integrated evolutionary programming

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控制理论与应用（英文版） 2004年第4期2卷 319-331页

作者： XiongLUO XiaopingFAN HengZHANG TefangCHEN CollegeofInformationScienceandEnoneermg CentralSouthUniversityChangshaHunan410083China

Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two main categories: optimum traveling time and optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. So far, there have been few planning algorithms designed to satisfy two performance indexes simultaneously. On the other hand, some deficiencies arise in the existing integrated optimi2ation algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators. In the algorithm, two object functions are designed based on the specific weight coefficient method and ' ideal point strategy. Moreover, based on the features of optimization problem, the intensified evolutionary programming is proposed to solve the corresponding optimization model. Especially, for the Stanford Robot,the high-quality solutions are found at a lower cost.

关键词： Trajectory planning Integrated optimization evolutionary programming Robot manipulator

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Metaheuristics for meltshop scheduling in the steel industry

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2007年第6期58卷 791-796页

作者： Huegler, P. A. Vasko, F. J. Lock Haven Univ Business Adm Comp Sci & Informat Technol Dept Clearfield PA 16830 USA Kutztown State Univ Kutztown PA 19530 USA

The scheduling of a meltshop at an integrated steel plant is a very complex and important logistical industrial problem. This problem requires the synchronization of several steelmaking furnaces, degassing facilities, ladle treatment stations, and continuous casters. In this paper, we discuss how an efficient domain-specific heuristic is combined with metaheuristic approaches in a prototype scheduling model. Specifically, given preliminary schedules for the continuous casters, the model determines the allocation, sequencing, and scheduling of batches of steel at the basic oxygen steelmaking furnaces, the degassing facilities, and the ladle treatment stations. It also makes the appropriate schedule modifications at the continuous casters. Computational results will be discussed.

关键词： steelmaking scheduling application evolutionary programming simulated annealing metaheuristics

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Automatic design of hierarchical Takagi-Sugeno type fuzzy systems using evolutionary algorithms

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2007年第3期15卷 385-397页

作者： Chen, Yuehui Yang, Bo Abraham, Ajith Peng, Lizhi Jinan Univ Sch Informat Sci & Engn Jinan 250022 Peoples R China Wuhan Univ Sci & Technol State Key Lab Adv Technol Mat Synth & Proc Wuhan 420023 Peoples R China Chung Ang Univ IITA Prof Program Sch Comp Sci & Engn Seoul 156756 South Korea

This paper presents an automatic way of evolving hierarchical Takagi-Sugeno fuzzy systems (TS-FS). The hierarchical structure is evolved using probabilistic incremental program evolution (PIPE) with specific instructions. The fine tuning of the IF-THEN rule's parameters encoded in the structure is accomplished using evolutionary programming (EP). The proposed method interleaves both PIPE and EP optimizations. Starting with random structures and rules' parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it further fine tunes the rules' parameters. It then goes back to improve the structure and the rules' parameters. This loop continues until a satisfactory solution (hierarchical TS-FS model) is found or a time limit is reached. The proposed hierarchical TS-FS is evaluated using some well known benchmark applications namely identification of nonlinear systems, prediction of the Mackey-Glass chaotic time-series and some classification problems. When compared to other neural networks and fuzzy systems, the developed hierarchical TS-FS exhibits competing results with high accuracy and smaller size of hierarchical architecture.

关键词： classification evolutionary programming hierarchical Takagi-Sugeno fuzzy systems (TS-FS) model probabilistic incremental program evolution algorithm system identification time-series prediction

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Bayesian training of neural networks using genetic programming

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PATTERN RECOGNITION LETTERS 2007年第12期28卷 1452-1458页

作者： Marwala, Tshilidzi Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa

Bayesian neural network trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. The algorithm proposed here has the ability to learn using samples obtained from previous steps merged using concepts of natural evolution which include mutation, crossover and reproduction. The reproduction function is the Metropolis framework and binary mutation as well as simple crossover, are also used. The proposed algorithm is tested on simulated function, an artificial taster using measured data as well as condition monitoring of structures and the results are compared to those of a classical MCMC method. Results confirm that Bayesian neural networks trained using genetic programming offers better performance and efficiency than the classical approach. (c) 2007 Elsevier B.V. All rights reserved.

关键词： Bayesian framework evolutionary programming neural networks

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Impact of recovered thermal energy management on the economics of operation of a grid-parallel PEM fuel cell power plant

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ELECTRIC POWER SYSTEMS RESEARCH 2007年第8期77卷 1056-1064页

作者： El-Sharkh, M. Y. Tanrioven, M. Rahman, A. Alam, M. S. Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

Management of recovered thermal energy with the object of achieving optimal cost of operation of a grid-parallel PEM fuel cell power plant (FCPP) is the focus of this paper. An economic model is presented which includes the operational cost, thermal recovery, power trade with the local grid, and selling of surplus thermal energy. Multiple operational strategies are developed using this model. The strategies are then investigated by estimating the quarter-hourly generated power, and the amount of recovered thermal power while satisfying the thermal and electrical load requirements. An evolutionary programming-based technique is used to solve for the optimal operational strategy. The model is tested using different seasonal load conditions. Results are encouraging and indicate viability of the proposed model. (c) 2006 Elsevier B.V. All rights reserved.

关键词： fuel cell PEM fuel cell evolutionary programming thermal recovery

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MEPAR-miner:: Multi-expression programming for classification rule mining

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第2期183卷 767-784页

作者： Baykasoglu, Adil Ozbakir, Lale Gaziantep Univ Dept Ind Engn TR-27310 Gaziantep Turkey Erciyes Univ Dept Ind Engn Kayseri Turkey

Classification and rule induction are two important tasks to extract knowledge from data. In rule induction, the representation of knowledge is defined as IF-THEN rules which are easily understandable and applicable by problem-domain experts. In this paper, a new chromosome representation and solution technique based on Multi-Expression programming (MEP) which is named as MEPAR-miner (Multi-Expression programming for Association Rule Mining) for rule induction is proposed. Multi-Expression programming (MEP) is a relatively new technique in evolutionary programming that is first introduced in 2002 by Oltean and Dumitrescu. MEP uses linear chromosome structure. In MEP, multiple logical expressions which have different sizes are used to represent different logical rules. MEP expressions can be encoded and implemented in a flexible and efficient manner. MEP is generally applied to prediction problems;in this paper a new algorithm is presented which enables MEP to discover classification rules. The performance of the developed algorithm is tested oil nine publicly available binary and n-ary classification data sets. Extensive experiments are performed to demonstrate that MEPAR-miner can discover effective classification rules that are as good as (or better than) the ones obtained by the traditional rule induction methods. It is also shown that effective gene encoding structure directly improves the predictive accuracy of logical IF-THEN rules. (c) 2006 Elsevier B.V. All rights reserved.

关键词： data mining classification rules multi-expression programming evolutionary programming

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Cultural algorithms, an alternative heuristic to solve the job shop scheduling problem

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ENGINEERING OPTIMIZATION 2007年第1期39卷 69-85页

作者： Rivera, Daniel Cortes Becerra, Ricardo Landa Coello, Carlos A. Coello CINVESTAV IPN Evolutionary Computat Grp Dept Ingn ElectrSecc Computac Mexico City 07300 DF Mexico

In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.

关键词： cultural algorithms evolutionary algorithms evolutionary programming job shop scheduling metaheuristics

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Dielectric filter optimal design suitable for microwave communications by using multiobjective evolutionary algorithms

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2007年第10期49卷 2324-2329页

作者： Goudos, S. K. Zaharis, Z. D. Salazar-Lechuga, M. Lazaridis, P. I. Gallion, P. B. Aristotle Univ Thessaloniki Telecommun Ctr Thessaloniki 54124 Greece Alexander Technol Educ Inst Thessaloniki Dept Elect Thessaloniki 57400 Greece Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Ecole Natl Super Telecommun 820 CNRS Unite Rech Assoc Dept Commun & Electron F-75634 Paris 13 France

A multiobjective evolutionary technique is applied to design dielectric fillers useful in communications technology. The optimal geometry of the filter is derived by utilizing two different multiobjective optimization algorithms. The first one is the Nondominated Sorting Genetic Algorithm-II (NSGA-II). Which is a popular multiobjective genetic algorithm. The second algorithm is based on multiobjective Particle Swarm Optimization with fitness sharing (MOPSO-fs). MOPSO-fs algorithim is a novel Pareto PSO algorithim that produces the Poreto from in a first and efficient way. In the present work. MOPSO-fs is compared with NSGA-II to optimize the geometry of the filters under specific requirements concerning the frequency response of the filters. Several examples are studied to exhibit the efficiency of the multiobjective optimal structures that can be used in practice. (c) 2007 Wiley Periodicals, Inc.

关键词： microwave filters dielectric filters multiobjective optimization pareto optimization particle swarm optimization evolutionary programming genetic algorithms

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The case for frame-based software engineering

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IEEE SOFTWARE 2007年第4期24卷 90-+页

作者： Bassett, Paul G. Cutter Consortium

A frame is an archetype—a model from which all similar things are made. Frame technology can synthesize any information structure (such as a program) from machine-adaptable frames. The author contrasts FT with object-oriented classes. By canonically defining each information structure in terms of its unique properties, frames avoid the complexities induced by code-level redundancies. Frames also solve the problem of how to regenerate domain-specific-language programs without destroying prior customizations. The article includes evidence of FT's efficacy, its impact on software's life cycle, elements of frame design, and an easy way to get started.

关键词： Object Oriented programming Software Reusability Software Engineering Frame Technology Generic Components Custom Information Structures Reuse Maximation Redundancy Minimization Software Life Cycle Software Engineering programming Profession Software Maintenance Redundancy Software Systems Software Quality Computer Industry Humans Statistics Organizing Reuse Models Automatic programming evolutionary programming Software Engineering Process

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