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Learning Bayesian network structure with immune algorithm

Journal of Systems Engineering and Electronics 2015年第2期26卷 282-291页

作者： Zhiqiang Cai Shubin Si Shudong Sun Hongyan Dui Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology School of Mechatronics Northwestern Polytechnical University

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

关键词： structure learning Bayesian network immune algorithm local optimal structure vaccination

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immune algorithm for Intelligent Controller in Braking System of Electric Railway Transport

Immune Algorithm for Intelligent Controller in Braking Syste...

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14th International Power Electronics and Motion Control Conference (EPE-PEMC)

作者： Levchenkov, Anatoly Gorobetz, Mikhail Ribickis, Leonids Balckars, Peteris Riga Tech Univ Inst Ind Elect & Elect Engn Riga Latvia Riga Tech Univ Inst Ind Elect & Elect Engn Riga Latvia Riga Tech Univ Inst Railway Transport Riga Latvia

ISBN: (纸本)9781424478545

immune algorithm is proposed for diagnostics controller of train's braking system. Purpose is to prevent accidents by reducing the human factor. Mathematical models and algorithms are developed. Results of computer experiments show the possibility of controller to detect and warn about changes in the system and to prevent emergencies immediately.

关键词： Electric railway transport intelligent device immune algorithm braking system

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immune algorithm FOR SUPERVISED CLUSTERING

IMMUNE ALGORITHM FOR SUPERVISED CLUSTERING

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5th IEEE International Conference on Cognitive Informatics (ICCI 2006)

作者： Xu, Lifang Mo, Hongwei Wang, Kejun Harbin Engn Univ Automat Coll Harbin Peoples R China

ISBN: (纸本)9781424404759

This paper centers on a novel data mining technique we term immune supervised clustering. Unlike traditional clustering, immune supervised clustering assumes that the examples are classified by immune algorithm. The goal of immune supervised clustering algorithm(ISCA) is to identify class-uniform clusters that have high probability densities. The experimental results suggest that ISCA, although runtime intensive, finds the best clusters in almost all experiments conducted.

关键词： supervised clustering immune algorithm clustering for classification

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Artificial immune algorithm for IIR filter design

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2005年第8期18卷 919-929页

作者： Kalinli, A Karaboga, N Erciyes Univ Fac Engn Dept Comp Engn TR-38039 Kayseri Turkey Erciyes Univ Fac Engn Dept Elect Engn TR-38039 Kayseri Turkey

Over the recent years, several studies have been carried out by the researchers to describe a general, flexible and powerful design method based on modern heuristic optimisation algorithms for infinite impulse response (IIR) digital filters since these algorithms have the ability of finding global optimal solution in a nonlinear search space. One of the modern heuristic algorithms is the artificial immune algorithm which implements a learning technique inspired by human immune system. However, the immune system has not attracted the same kind of interest from researchers as other heuristic algorithms. In this work, an artificial immune algorithm is described and applied to the design of IIR filters, and its performance is compared to that of genetic and touring ant colony optimisation algorithms. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： immune algorithm genetic algorithm ant colony algorithm IIR filter design

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Scheduling of cogeneration plants considering electricity wheeling using enhanced immune algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2005年第1期27卷 31-38页

作者： Chen, SL Tsay, MT Gow, HJ Cheng Shiu Univ Dept Elect Engn Kaohsiung 833 Taiwan

A new method based on immune algorithm (IA) is presented to solve the scheduling of cogeneration plants in a deregulated market. The objective function includes fuel cost, population cost, and electricity wheeling cost, subjective to the use of mixed fuels, operational limits, emissions constraints, and transmission line flow constraints. Enhanced immune algorithm (EIA) is proposed by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Table lists with heuristic rules are also employed in the searching process to enhance the performance. EIA is also compared with the original IA. Test results verify that EIA can offer an efficient way for cogeneration plants to solve the problem of economic dispatch, environmental protection, and electricity wheeling. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： immune algorithm cogeneration plants deregulated market

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Efficient immune algorithm for optimal allocations in series-parallel continuous manufacturing systems

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JOURNAL OF INTELLIGENT MANUFACTURING 2012年第5期23卷 1603-1619页

作者： Massim, Y. Yalaoui, F. Chatelet, E. Yalaoui, A. Zeblah, A. Univ Djillali Liabes Sidi Bel Abbes Gumbeta 22000 Sidi Bel Abbes Algeria Univ Technol Troyes Inst Charles Delaunay CNRS FRE 2848LOSI Troyes France Univ Technol Troyes Inst Charles Delaunay CNRS FRE 2848LM2S Troyes France

This paper uses an immune algorithm (IA) meta-heuristic optimization method to solve the problem of structure optimization of series-parallel production systems. In the considered problem, redundant machines and buffers in process are included in order to attain a desirable level of availability. A procedure which determines the minimal cost system configuration is proposed. In this procedure, multiple choices of producing machines and buffers are allowed from a list of product available in the market. The elements of the system are characterized by their cost, estimated average up and down times, productivity rates and buffers capacities. The availability is defined as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. The proposed meta-heuristic is used as an optimization technique to seek for the optimal design configuration. The advantage of the proposed IA approach is that it allows machines and buffers with different parameters to be allocated.

关键词： Manufacturing system Buffers Meta-heuristic optimization immune algorithm Availability evaluation Optimal design

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A hybrid multi-objective immune algorithm for predictive and reactive scheduling

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JOURNAL OF SCHEDULING 2017年第2期20卷 165-182页

作者： Paprocka, Iwona Skolud, Bozena Silesian Tech Univ Inst Engn Proc Automat & Integrated Mfg Syst Ul Konarskiego 18a PL-44100 Gliwice Poland

The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Shifted Gap-Reduction. This paper presents the research results and computer simulations.

关键词： Predictive Reactive scheduling Multiobjective optimization immune algorithm Preventive maintenance

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A hybrid quantum-inspired immune algorithm for multiobjective optimization

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APPLIED MATHEMATICS AND COMPUTATION 2011年第9期217卷 4754-4770页

作者： Gao, Jiaquan Wang, Jun Zhejiang Univ Technol Zhijiang Coll Hangzhou 310024 Zhejiang Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

This study suggests a novel quantum immune algorithm for finding Pareto-optimal solutions to multiobjective optimization problems based on quantum computing and immune system. In the proposed algorithm, there are distinct characteristics as follows. First, the encoding method is based on Q-bit representation, and thus a chaos-based approach is suggested to initialize the population. Second, a new chaos-based rotation gate and Q-gates are presented to perform mutation and improve the quality of the population, respectively. Finally, especially, a new truncation algorithm with similar individuals (TASI) is utilized to preserve the diversity of the population. Also, a new selection operator is proposed to create the new population based on TASI. Simulation results on six standard problems (ZDT6, CP, SP, VNT, OSY and KIT) show the proposed algorithm is able to find a much better spread of solutions and has better convergence near the true Pareto-optimal front compared to the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II). (C) 2010 Elsevier Inc. All rights reserved.

关键词： immune algorithm Multiobjective Optimization Similar individuals Quantum algorithm

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A novel chaos danger model immune algorithm

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2013年第11期18卷 3046-3060页

作者： Xu, Qingyang Wang, Song Zhang, Li Liang, Ying Shandong Univ Weihai Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

Making use of ergodicity and randomness of chaos, a novel chaos danger model immune algorithm (CDMIA) is presented by combining the benefits of chaos and danger model immune algorithm (DMIA). To maintain the diversity of antibodies and ensure the performances of the algorithm, two chaotic operators are proposed. Chaotic disturbance is used for updating the danger antibody to exploit local solution space, and the chaotic regeneration is referred to the safe antibody for exploring the entire solution space. In addition, the performances of the algorithm are examined based upon several benchmark problems. The experimental results indicate that the diversity of the population is improved noticeably, and the CDMIA exhibits a higher efficiency than the danger model immune algorithm and other optimization algorithms. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Global optimization immune algorithm Danger theory Chaos

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Three-phase balancing of distribution feeders using immune algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2008年第3期2卷 383-392页

作者： Huang, M. -Y. Chen, C. -S. Lin, C. -H. Kang, M. -S. Chuang, H. -J. Huang, C. -W. Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Kao Yuan Univ Dept Elect Engn Lu Chu Taiwan Taiwan Power Co Power Res Inst Taipei Taiwan

The immune algorithm (IA) is proposed to derive the rephasing strategy arrangement of laterals and distribution transformers to enhance three-phase balancing of distribution systems. The multi-objective function is formulated by considering the unbalance of phasing currents, the customer service interruption cost (CIC) and labour cost to perform the optimal rephasing strategy. For each feasible rephasing strategy, the number of customers affected with total interruption load demand and outage duration time are used to calculate the impact of system reliability because of rephasing engineering works. To demonstrate the effectiveness of the proposed methodology, a practical distribution feeder in Taipower with 271 customers is selected for computer simulation. By minimising the objective function subjected to the operation constraints, the rephasing strategy has been derived by selecting the laterals and distribution transformers for phasing adjustment. It is found that the neutral current of test feeder has been reduced to be less than the neutral overcurrent limit by executing the rephasing of laterals and distribution transformers.

关键词： customer service interruption cost labour cost immune algorithm multi-objective function system reliability Distribution networks distribution transformers Transformers and reactors power distribution reliability distribution feeders three-phase balancing power transformers rephasing strategy arrangement

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