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A Data-Based Fuzzy Cognitive Map Mining Method Using DE-SQP algorithm

A Data-Based Fuzzy Cognitive Map Mining Method Using DE-SQP ...

Asia Simulation Conference/International Conference on System Simulation and Scientific Computing (AsiaSim and ICSC 2012)

作者： Shou, Wenhui Fan, Wenhui Liu, Boyuan Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9783642343865

Fuzzy Cognitive Map (FCM), which was first proposed by Kosko in 1986, is a novel tool for knowledge description and management. In this paper, we propose a new approach to mine the FCMs on the basis of data resource. First, raw data stored in the databases is preprocessed and converted to a fixed interval. Then, we adopt a novel hybrid optimization algorithm DE-SQP to produce the optimal weight matrix, whose goal is to make the weights fit the historical data best. At last, we apply the proposed method to solve a real-world problem. The experimental results show that the method is effective and efficient to mine the casual relationships hidden in the data resources by using the hybrid algorithm DE-SQP and the construction of FCMs gets rid of the intervention of domain experts, which guarantees the objectivity and completeness of the FCMs.

关键词： Fuzzy Cognitive Map Data Mining hybrid optimization algorithm DE-SQP

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Propulsion performance diagnostics and optimization based on the estimation of measurement biases and hybrid optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2011年第G7期225卷 821-830页

作者： Zhang, J. Tang, H. L. Shen, G. Z. Yang, L. Y. Beijing Univ Aeronaut & Astronaut Natl Key Lab Sci & Technol Holist Control Beijing 100191 Peoples R China Beijing Univ Aeronaut & Astronaut Sch Jet Prop Beijing 100191 Peoples R China

This article deals with performance diagnostics and optimization of aircraft propulsion systems. To eliminate the adverse effects of unknown measurement biases on performance diagnostics, a new design approach based on measurement biases estimation is proposed. In this approach, measurement biases and the component deviation parameters (CDPs) are treated as a part of the augmented state vector, and multiple Kalman filters are designed based on different biases hypotheses to estimate the augmented states. Through the estimated measurement biases, the effects of actual biases are eliminated and accurate performance diagnostics is achieved. Moreover, considering the synthesis influence of linearization errors and initial values on the conventional linear programming (LP) method, a hybrid optimization algorithm based on LP and sequential quadratic programming (SQP) is investigated to improve the optimization accuracy and convergence speed. This algorithm uses a parallel subregion LP method to obtain the global feasible initial value, and then utilizes the SQP algorithm to search for the optimal solution. Simulations are carried out at cruise-operating conditions. The results demonstrate that the CDPs are estimated accurately, and the propulsion performance and online optimization ability are also improved.

关键词： propulsion system performance diagnostics and optimization measurement biases estimation hybrid optimization algorithm

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Improved Genetic algorithm and Its Application in Parameter optimization for Certain Aeroengine Compressor Guide Vane Regulator

Improved Genetic Algorithm and Its Application in Parameter ...

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IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Peng, Kai Fan, Ding Fu, Jiangfeng Zhang, Lei NW Polytech Univ Sch Power & Energy Xian 710072 Peoples R China

ISBN: (纸本)9781457703218

An improved genetic algorithm (Fuzzy Adaptive Simulated Annealing Genetic algorithm with Gradient direction, GFASAGA) will be proposed in this paper, whose global superlinear convergence properties was analyzed by means by Markov chain etc. Certain fuzzy aeroengine compressor guide vane controller parameters of the regulator were optimized by GFASAGA, standard genetic algorithm (SGA) and customized hybrid optimization algorithm in iSIGHT comparatively, then simulation results show that: the improved genetic algorithm is of good characteristics, such as global search, evolutionary rapidity and so on;the ultimate guide vane regulator formed by semi-physical simulation is provided with good static and dynamic characteristics.

关键词： compressor guide vane regulator fuzzy control genetic algorithm hybrid optimization algorithm

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hybrid anti-prematuration optimization algorithm

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Journal of Systems Engineering and Electronics 2010年第3期21卷 503-508页

作者： Qiaoling Wang Xiaozhi Gao Changhong Wang Furong Liu Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin 150001 P. R. China Department of Electrical Engineering Helsinki University of Technology Otakaari 5 A Espoo 02150 Finland

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization *** paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear *** proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,*** operator,cloning operator,suppression operator,and receptor editing *** swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune *** simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence *** is also employed to cope with a real-world optimization problem.

关键词： hybrid optimization algorithm artificial immune system（AIS） particle swarm optimization（PSO） clonal selection anti-prematuration.

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Modified electromagnetism-like algorithm and its application to slope stability analysis

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Journal of Central South University 2011年第6期18卷 2100-2107页

作者：张科曹平 School of Resources and Safety Engineering Central South University

In the view of the disadvantages of complex method (CM) and electromagnetism-like algorithm (EM), complex electromagnetism-like hybrid algorithm (CEM) was proposed by embedding complex method into electromagnetism-like algorithm as local optimization algorithm. CEM was adopted to search the minimum safety factor in slope stability analysis and the results show that CEM holds advantages over EM and CM. It combines the merits of two and is more stable and efficient. For further improvement, two CEM hybrid algorithms based on predatory search (PS) strategies were proposed, both of which consist of modified algorithms and the search area of which is dynamically adjusted by changing restriction. The CEM-PS1 adopts theoretical framework of original predatory search strategy. The CEM-PS2 employs the idea of area-restricted search learned from predatory search strategy, but the algorithm structure is simpler. Both the CEM-PS1 and CEM-PS2 have been demonstrated more effective and efficient than the others. As for complex method which locates in hybrid algorithm, the optimization can be achieved at a convergence precision of 1×10-3, which is recommended to use.

关键词： slope stability hybrid optimization algorithm complex method electromagnetism-like algorithm predatory searchstrategy

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Estimation of Geoacoustic Properties of Marine Sediment Using a hybrid Differential Evolution Inversion Method

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IEEE JOURNAL OF OCEANIC ENGINEERING 2010年第1期35卷 59-69页

作者： Jiang, Yong-Min Chapman, N. Ross Gerstoft, Peter Univ Victoria Sch Earth & Ocean Sci Victoria BC V8W 3V6 Canada Univ Calif San Diego Scripps Inst Oceanog Marine Phys Lab La Jolla CA 92093 USA

This paper reports the inversion of midfrequency (1500-4500 Hz) chirps from a short-range transmission experiment conducted on the New Jersey Continental Shelf during the 2006 Shallow Water Experiment (SW06). The source was held at different depths and the sound signals were recorded at a vertical line array to investigate the interactions with the sea bottom at various grazing angles. Strong reflections from the sediment layer were seen in the data for all of the sources. Due to the presence of complex microstructures in the thermocline of the oceanic sound-speed profile, fluctuations both in amplitude and arrival time of the direct path arrivals were observed. Time variation of the water-column environment was also evident during the source transmissions. To mitigate the effects of the ocean environment on the seabed property estimation, a multistage optimization inversion was employed. The sound speed and the experimental geometry were inverted first using only the travel times of the water-column arrivals. The bottom sound speed and the sediment layer thickness were then inverted by matching the travel times of the bottom and sub-bottom reflections. The average of the estimated values for the sediment sound speed is 1598 m/s, consistent with in situ measurements from other experiments in the vicinity.

关键词： Differential evolution (DE) downhill simplex (DHS) empirical orthogonal function (EOF) geoacoustic inversion hybrid optimization algorithm sediment property travel-time inversion

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Parameter Estimation of Disk Drive Servo System Using A hybrid Simplex Differential Evolution algorithm

Parameter Estimation of Disk Drive Servo System Using A Hybr...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Wang, Yaonan Wu, Lianghong Yuan, Xiaofang Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781424467129

To accurately maintain the head position along the center of the track, determining the optimal parameters of the disk drive servo control system is one of most important works. In this paper, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the differential evolution (DE) algorithm was presented to the optimum parameter estimation of a disk drive servo system. This hybrid procedure performed the exploration with DE and the exploitation with the NM simplex method. Three experiments under the different conditions of command signal, sampling period and measuring noises were used to test the effectiveness of the proposed approach. The experiment results demonstrate that the hybrid optimization algorithm is superior to the two original search techniques (i.e. NM and DE) in terms of solution quality and convergence rate, and is an effective method for the parameter estimation of a control system.

关键词： Parameter estimation Disk drive servo system Differential evolution Simplex method hybrid optimization algorithm

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A hybrid Simplex Differential Evolution algorithm

A Hybrid Simplex Differential Evolution Algorithm

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22nd Chinese Control and Decision Conference

作者： Wu, Lianghong Wang, Yaonan Yuan, Xiaofang Zhou, Shaowu Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781424451821

According to the disadvantage of slow convergence rate of the basic differential evolution (DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the basic DE algorithm is presented in this paper. This hybrid procedure performed the exploration with DE and the exploitation with the NM simplex method. Sensitivity to the control parameters of the proposed approach is analyzed. The computational results on several classical Benchmarks nonlinear complex functions show that the hybrid optimization algorithm is superior to the two original search techniques (i.e. NM and DE) in terms of solution quality and convergence rate. Compared with other DE variants, the proposed algorithm has better convergence performance and robustness. The Wilcoxon non-parametric statistical tests also confirm the above claims.

关键词： Differential evolution Simplex method hybrid optimization algorithm non-parametric statistical

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An Effective GSA Based Memetic algorithm for Permutation Flow Shop Scheduling

An Effective GSA Based Memetic Algorithm for Permutation Flo...

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2010 IEEE World Congress on Computational Intelligence

作者： Li, Xiangtao Wang, Jianan Zhou, Junping Yin, Minghao Northeastern Normal Univ Coll Comp Sci Changchun 130117 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Northeastern Normal Univ Rice Univ Coll Comp Sci Changchun 130117 Peoples R China

ISBN: (纸本)9781424481262

the permutation flow shop problem (PFSSP) is a well-known difficult combinatorial optimization problem. In this paper, we present a new hybrid optimization algorithm named SIGSA to solve the PFSSP. This algorithm is composed by the LRV rule, SA-based local search and IIS-based local search. First, to make GSA suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in GSA to the discrete job permutation. Second, to enhance the searching capability, the SA-based local search is designed to help the algorithm to escape from local minimum. Then, the IIS-based local search is used for enhancing the individuals in GSA with a certain probability. Additionally, Comparison with other results in the literature shows that the SIGSA is an efficient and effective approach for the PFSSP.

关键词： GSA based memetic algorithm IIS-based local search LRV rule PFSSP SA-based local search combinatorial mathematics combinatorial optimization problem computational complexity discrete job permutation flow shop scheduling genetic algorithms hybrid optimization algorithm permutation flow shop scheduling search problems searching capability enhancement

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An effective hybrid optimization algorithm for the flow shop scheduling problem

An effective hybrid optimization algorithm for the flow shop...

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IEEE International Conference on Information Acquisition

作者： Sun Kai Yang Genke Shanghai Jiao Tong Univ Dept Automat Shanghai 200030 Peoples R China

ISBN: (纸本)1424405289

This paper presents a new multi-swarm co-evolutionary algorithm named parallel particle swam optimization (PPSO) on the basis of standard PSO algorithm. Simulated annealing (SA) algorithm was introduced to increase escaping probability from local optima. By reasonably combining the PPSO with SA, we develop a general, fast and easily implemented hybrid optimization algorithm, and apply it to solve flow shop scheduling problem. Comparing results indicate that the new hybrid method is an effective and competitive approach for the flow shop scheduling problem.

关键词： parallel particle swarm optimization simulated annealing hybrid optimization algorithm flow shop scheduling problem

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