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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004

作者： Wu, Chunguo Xiang, Wei Liang, Yanchun Lee, Heow Pueh Zhou, Chunguang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun130012 China Institute of High Performance Computing Singapore117528 Singapore

ISBN: (纸本)3540228179

An operation template is proposed in this paper for describing the mapping between operations and a subset of natural numbers. With such operation template, a job shop scheduling problem (JSSP) can be transformed into a traveling salesman problem (TSP), hence the integer-coding genetic algorithm for TSP can be easily applied and modified. A decoding strategy, called virtual job shop, is proposed to evaluate the fitness of the individual in GA population. The integration of the operation template and virtual job shop makes the existing integer-coding GA possible for solving an extension of a classical job shop scheduling problem. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Traveling salesman problem

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The geometric constraint solving based on memory particle swarm algorithm

The geometric constraint solving based on memory particle sw...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chun-Hong Cao Wen-Hui Li Yong-Jian Zhang Rong-Qing Yi College of computer science and technology Jilin University Changchun China Department of Information and Electronical Engineering Shandong Institute of Architecture and Engineering Jinan China

ISBN: (纸本)0780384032

Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. PSO is an evolution computing method. It searches the solution space by creating a better next swarm. The new swarm is produced based on the new individuals. Memory particle swarm algorithm is a PSO algorithm that adds a memory influence. We introduce MPSO into geometric constraint solving. The purpose of the added memory feature is to maintain spread and therefore diversity by providing individual specific alternate target points to be used at times instead of the current local best position. The experiment indicates that the algorithm is effective.

关键词： Particle swarm optimization Nonlinear equations Constraint optimization Educational institutions computer science computer architecture Stability Matrix decomposition Machine learning

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Study on optimization of agent initial positions in land combat simulation

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自然科学进展(英文版) 2004年第3期14卷 257-261页

作者： WU Chunguo LIANG Yanchun LEE Heow Pueh LU Chun Yang Xiaowei College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore Institute of High Performance Computing Singapore 117528 Singapore Department of Applied Mathematics South China University of Technology Guangzhou 510640 China Centre for ACES Department of Mechanical Engineering National University of Singapore 119260 Singapore

The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A novel method for the reduction of support vectors in the support vector machine (SVM) is presented. The optimization on the width of the Gaussian kernel function and the combination of the SVM with the radial basis function neural network are performed in the proposed method. Simulation results show that the proposed method can improve the running efficiency drastically compared with that using the traditional SVM with the same precision. We also summarize and present some experiences and trends in the study on the optimization problem in land combat simulation.

关键词： multi-agent support vector machine radial basis function genetic algorithm regression

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Modified particle swarm optimization based on space transformation for solving traveling salesman problem

Modified particle swarm optimization based on space transfor...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Wei Pang Kang-Ping Wang Chun-Guang Zhou Long-Jiang Dong Ming Liu Hong-Yan Zhang Jian-Yu Wang College of Computer Science and Technology Jilin University Changchun China Department of Electric Engineering Changchun Institute of Technology Changchun China

ISBN: (纸本)0780384032

A modified particle swarm optimization was proposed to solve traveling salesman problem (TSP). The algorithm searched in the Cartesian continuous space, and constructed a mapping from continuous space to discrete permutation space of TSP, thus to implement the space transformation. Moreover, local search technique was introduced to enhance the ability to search, and chaotic operations were employed to prevent the particles from falling into local optima prematurely. Finally four benchmark problems in TSPLIB were tested to evaluate the performance of the algorithm. Experimental results indicate that the algorithm can find high quality solutions in a comparatively short time.

关键词： Particle swarm optimization Space exploration Traveling salesman problems Space technology Cities and towns Chaos Educational institutions computer science Benchmark testing Random number generation

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Generalized chromosome genetic algorithm for generalized traveling salesman problems and its applications for machining

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Physical Review E 2004年第1期70卷 016701-016701页

作者： Chunguo Wu Yanchun Liang Heow Pueh Lee Chun Lu College of Computer Science Jilin University Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 Singapore 119260 Singapore

Traveling salesman problems (TSP) and generalized traveling salesman problems (GTSP) are two kinds of well known and challenging combinatorial optimization problems with much diversified application fields. Between the two application problems the GTSP is more complex than TSP. Many researchers have studied TSP extensively, but relatively fewer studies pay attention to GTSP, and also its solution using genetic algorithm (GA). In this paper, the structure of conventional chromosome is generalized to be a chromosome termed as a generalized chromosome (GC). A genetic scheme named as generalized-chromosome-based genetic algorithm (GCGA) is also presented. The proposed GCGA enables GTSP and TSP to be solved under a uniform algorithm mode. Forty one benchmark test problems have been solved with the known optimal solutions using the proposed algorithm to verify its validity. The test results show that GCGA can directly solve GTSP without the need of intermediate transformation to TSP.

关键词： Computational geometry

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Genetic algorithm application on the job shop scheduling problem

Genetic algorithm application on the job shop scheduling pro...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： C.G. Wu X.L. Xing H.P. Lee C.G. Zhou Y.C. Liang College of Computer Science and Technology Jilin University Changchun China Institute of High Performance Computing Singapore Department of Optoelectronic Engineering Beijing Institute of Technology Beijing China

Based on the concepts of operation template and virtual job shop, this paper attempts to solve several job shop scheduling problems with different scale and analyzes the relationship among the population size, mutation probability, the number of evolving generations and the complexity of the undertaking problem visually by using the trend chart of the fitness curves. This visual analysis could provide some referencing information for the adjustment of genetic algorithm running parameters.

关键词： Genetic algorithms Job shop scheduling Genetic mutations Application software Processor scheduling computer science High performance computing Performance analysis Information analysis Algorithm design and analysis

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Short-term load forecasting using neural network with principal component analysis

Short-term load forecasting using neural network with princi...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Xin-Chen Guo Zhou-Yi Chen Hong-Wei Ge Yan-Chun Liang College of Computer Science and Technology Jilin University Changchun China Department of Mathematics and Physics Science Northeast Institute of Electric Power Engineering Jilin China The 8th Research Institute of Air Force Beijing China

ISBN: (纸本)0780384032

A neural-network-based (NN-based) approach for short-term load forecasting of electrical power is proposed. The principal component analysis (PCA) technique is used to reduce the original electric load variables to several characteristic variables. A single parameter dynamic search algorithm (SPDS) is employed to train the NN. Since the training sample sets can be chosen before forecasting, the interference of the non-correlative samples for the forecasting can be avoided. The effectiveness and the feasibility of on line forecasting of the proposed method are examined using simulated experiments.

关键词： Load forecasting Neural networks Principal component analysis Weather forecasting Economic forecasting Demand forecasting Machine learning algorithms Heuristic algorithms Power system simulation Power system dynamics

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A modified particle swarm optimization for combining forecasting

A modified particle swarm optimization for combining forecas...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： X.Y. Feng L.M. Wan Y.C. Liang Y.F. Sun H.P. Lee Y. Wang Kollege of Computer Science and Technology Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China The 8th Research Institute of Air Force Beijing China Institute of High Performance Computing Singapore Singapore

A modified particle swarm optimization (PSO) algorithm is proposed. Linear constraints in the PSO are added to satisfy the normalization conditions for different problems. A hybrid algorithm based on the modified PSO and combining forecasting is presented. Combining forecasting can improve the forecasting accuracy through combining different forecasting methods. The effectiveness of the algorithm is demonstrated through the prediction on the sunspots and the stocks data. Simulated results show that the hybrid algorithm can improve the forecasting accuracy to a great extent.

关键词： Particle swarm optimization Computational modeling computer science Educational technology Laboratories Military computing Knowledge engineering computer science education High performance computing Predictive models

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A discrete PSO method for generalized TSP problem

A discrete PSO method for generalized TSP problem

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： X.H. Zhi X.L. Xing Q.X. Wang L.H. Zhang X.W. Yang C.G. Zhou Y.C. Liang Coll. of Comput. Sci. & Technol. Jilin Univ. Changchun China Department of Optoelectronic Engineering Beijing Institute of Technology Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Information Technology College of Administration of Jilin Province Changchun China Department of Applied Mathematics South China University of Technology Guangzhou China

A novel discrete particle swarm optimization (PSO) method is presented to solve the generalized traveling salesman problem (GTSP). The "generalized vertex" is employed to represent the problem, by which the GTSP and TSP can be handled in a uniform style. An uncertain searching strategy and local searching techniques are also employed to accelerate the convergent speed. Numerical results show the effectiveness of the proposed method.

关键词： Particle swarm optimization Traveling salesman problems Educational institutions Dynamic programming Biological cells computer science Information technology Mathematics Acceleration Application software

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A hybrid algorithm based on PSO and genetic operation and its applications for cutting stock problem

A hybrid algorithm based on PSO and genetic operation and it...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： J.Q. Jiang X.L. Xing X.W. Yang Y.C. Liang College of Computer Science and Technology Jilin University Changchun China College of Mathematics and Computer Science National University of Inner Mongolia Tongliao China Department of Optoelectronic Engineering Beijing Institute of Technology Beijing China Department of Applied Mathematics South China University of Technology Guangzhou China

A hybrid algorithm based on particle swarm optimization (PSO) and genetic operations is presented and applied to the constrained two-dimensional non-guillotine cutting stock problem. A converting approach similar to the bottom left (BL) algorithm is also used to map the cutting pattern to the actual layout. Simulations show that the proposed algorithm reduces the probability of trapping in the local optimum and is effective for dealing with the cutting stock problem.

关键词： Particle swarm optimization Application software computer science Mathematics Constraint optimization Educational institutions Genetic engineering Production Glass Steel

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