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7th International Symposium on Instrumentation and Control Technology

作者： Xing, Xiaojun Yuan, Dongli Yan, Jianguo NW Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9780819473622

In order to design full-envelop flight control law for a certain helicopter, a novel method based on multi-objective genetic algorithm (MOGA) is put forward. In this method, the design of flight control law is viewed as a multi-objective optimization problem(MOP) where obtaining optimal performances in each designed flight envelop are treated as sub-objective and that in all designed flight envelop as objective, flight control law's parameters to be searched as decision variables, corresponding performance criteria as constraints. For the MOP, MOGA is used to get the optimal parameters of flight control law in all designed flight envelops. Finally, the novel method is applied to the flight control law's design for the helicopter's pitch motion, the simulation results show that the parameters are feasible and the performances are satisfactory, which further prove that the method is effective.

关键词： multi-objective genetic algorithm Helicopter Flight control law

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Effects of diversity control in single-objective and multi-objective genetic algorithms

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JOURNAL OF HEURISTICS 2007年第1期13卷 1-34页

作者： Chaiyaratana, Nachol Piroonratana, Theera Sangkawelert, Nuntapon King Mongkuts Inst Technol N Bangkok Res & Dev Ctr Intelligent Syst Bangkok 10800 Thailand King Mongkuts Inst Technol N Bangkok Dept Prod Engn Bangkok 10800 Thailand King Mongkuts Inst Technol N Bangkok Dept Elect Engn Bangkok 10800 Thailand

This paper covers an investigation on the effects of diversity control in the search performances of single-objective and multi-objective genetic algorithms. The diversity control is achieved by means of eliminating duplicated individuals in the population and dictating the survival of non-elite individuals via either a deterministic or a stochastic selection scheme. In the case of single-objective genetic algorithm, onemax and royal road R-1 functions are used during benchmarking. In contrast, various multi-objective benchmark problems with specific characteristics are utilised in the case of multi-objective genetic algorithm. The results indicate that the use of diversity control with a correct parameter setting helps to prevent premature convergence in single-objective optimisation. Furthermore, the use of diversity control also promotes the emergence of multi-objective solutions that are close to the true Pareto optimal solutions while maintaining a uniform solution distribution along the Pareto front.

关键词： benchmarking diversity control multi-objective genetic algorithm single-objective genetic algorithm

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Optimal corrective actions for power systems using multi-objective genetic algorithms

Optimal corrective actions for power systems using multi-obj...

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42nd International Universities Power Engineering Conference

作者： El Ela, Adel A. Abou El-Din, Ashraf Zin Spea, Shaimaa R. Minofiya Univ Dept Elect Engn Fac Engn Menoufia Egypt

ISBN: (纸本)9781905593361

In this paper, optimal corrective control actions are presented to restore the secure operation of power system for different operating conditions. genetic algorithm (GA) is one of the modern optimization techniques, which has been successfully applied in various areas in power systems. Most of the corrective control actions involve simultaneous optimization of several objective functions, which are competing and conflicting each other. The multi-objective genetic algorithm (MOGA) is used to optimize. the corrective control actions. Three different procedures based on GA and MOGA are proposed to alleviate the violations of the overloaded lines and minimize the transmission line losses. The first procedure is based on corrective switching of the transmission lines and generation re-dispatch. The second procedure is carried out to determine the optimal siting and sizing of distributed generation (DG). While, the third procedure is concerned into solving the generation-load imbalance problem using load shedding. Numerical simulations are carried out on two test systems in order to examine the validity of the proposed procedures.

关键词： corrective control actions multi-objective genetic algorithm generation re-dispatch distributed generation load shedding

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multi-objective optimization of an auto panel drawing die face design by mesh morphing

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COMPUTER-AIDED DESIGN 2007年第10期39卷 863-869页

作者： Liu, Wei Yang, Yuying Harbin Inst Technol Sch Mat Sci & Engn Harbin 150006 Peoples R China

In order to facilitate the tryout or simulation process at the end of a manual auto panel drawing die face design process, we use finite element analysis (FEA) and a multi-objective genetic algorithm (MOGA) to find all the Pareto optimal solutions in one go and to achieve the optimal design of an auto panel drawing die face instead of transforming multi-objective functions into a single objective function, and employ a novel mesh morphing technique to achieve fast modification of parametric or non-parametric addendum surfaces and binder surfaces on drawing die faces without going back to CAD for reconstruction of geometric models or to FEA for remodeling. We use an auto panel drawing die face design process as an example to illustrate the application and effectiveness of this proposed approach, and come to the conclusion that the proposed approach is more effective than the traditional manual FEA method and the 'trial-and-error' approach in optimizing an auto panel drawing die face design. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm mesh morphing die face auto panel finite element analysis (FEA)

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Optimal robust linearized impulsive rendezvous

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AEROSPACE SCIENCE AND TECHNOLOGY 2007年第7-8期11卷 563-569页

作者： Tang, Guo-Jin Luo, Ya-Zhong Li, Hai-Yang Natl Univ Def Techncol Coll Aerosp & Mat Engn Changsha 410073 Peoples R China

The optimization of time-fixed linearized impulsive rendezvous with control uncertainty is investigated. One performance index related to the variances of the terminal state error is defined as the performance index of robustness which is calculated by linear covariance method. The two-objective optimization problem of minimizing the total characteristic velocity and the performance index of robustness is formulated based on the Clohessy-Wiltshire (C-W) system and solved by the nondominated sorting genetic algorithm. The Pareto-optimal solution sets of one homing rendezvous mission are provided and the Pareto optimality is verified by comparing with the fuel-optimal and the robustness-optimal solutions. It is shown that the proposed approach can quickly investigate the relation between the fuel cost and the trajectory robustness, besides evaluate different rendezvous maneuver schemes. (c) 2007 Elsevier Masson SAS. All rights reserved.

关键词： impulsive rendezvous uncertainty robust optimization multi-objective genetic algorithm

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A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2007年第4期53卷 642-666页

作者： Rahimi-Vahed, Alireza Mirzaei, Alil Hossein Univ Tehran Dept Ind Engn Tehran Iran Tarbiat Modares Univ Dept Ind Engn Tehran Iran

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers' demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： mixed-model assembly line multi-objective sequencing problem multi-objective shuffled frog-leaping algorithm bacterial optimization multi-objective genetic algorithm

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A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem

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SOFT COMPUTING 2007年第10期11卷 997-1012页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Rabbani, M. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran

The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.

关键词： mixed-model assembly line multi-objective sequencing problem just-in-time multi-objective particle swarm multi-objective genetic algorithm

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GA-fuzzy control of smart base isolated benchmark building using supervisory control technique

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ADVANCES IN ENGINEERING SOFTWARE 2007年第7期38卷 453-465页

作者： Kim, Hyun-Su Roschke, Paul N. Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Sungkyunkwan Univ Dept Architectural Engn Suwon South Korea

The effectiveness of a supervisory fuzzy control technique for reduction of seismic response of a smart base isolation system is investigated in this study. To this end, a first generation, base isolated, benchmark building is employed for numerical simulation. The benchmark structure under consideration has eight stories and an irregular plan. Furthermore it is equipped with low damping elastomeric bearings and magnetorheological (MR) dampers for seismic protection. The proposed control technique employs a hierarchical structure of fuzzy logic controllers (FLC) consisting of two lower-level controllers (sub-FLC) and a higher-level supervisory controller. One sub-FLC has been optimized for near-fault earthquakes and the other sub-FLC is well-suited for far-fault earthquakes. These sub-FLCs are optimized by use of a multi-objective genetic algorithm. Four objectives, i.e. reduction of peak superstructure acceleration, peak isolation system deformation, RMS superstructure acceleration and RMS isolation system deformation are used in a multi-objective optimization process. When an earthquake is applied to the benchmark building, each of the sub-FLCs provides different command voltages for the semi-active controllers and the supervisory fuzzy controller appropriately combines the two command voltages based on a fuzzy inference system in real time. Results from numerical simulations demonstrate that isolation system deformation as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique in comparison with a sample clipped optimal controller. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： smart base isolation benchmark control problem supervisory fuzzy control multi-objective genetic algorithm MR damper

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Fast multi-objective genetic algorithm based on clustering

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2004年第7期41卷 1081-1087页

作者： Zheng, Jin-Hua Shi, Zhong-Zhi Xie, Yong Lab. of Intelligent Info. Proc. Inst. of Comp. Technol. Chinese Acad. of Sci. Beijing 100080 China Coll. of Info. Eng. Xiangtan Univ. Xiangtan 411105 China

multi-objective genetic algorithm based on Pareto optimum is much suitable for solving multi-objective optimization problems. The relations between individuals and some features about these relations are discussed. It is proved that the individuals of an evolutionary population can be classified by the idea of quick sort. At the same time, the approach to maintain diversity of solutions by clustering algorithms is discussed, and the clustering algorithm based on hierarchical aggregation is also discussed. Then by using the quick sort algorithm and the clustering procedure, an algorithm of constructing a new evolutionary population is proposed. It is shown by theoretic analysis and experimental results that the convergent speed of the algorithm discussed is more efficient than the other existing algorithms.

关键词： Clustering multi-objective genetic algorithm multi-objective optimization

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A multi-objective particle swarm for a flow shop scheduling problem

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2007年第1期13卷 79-102页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particle's position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.

关键词： bi-criteria flow shop scheduling problem permutation flow shop multi-objective particle swarm multi-objective genetic algorithm

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