教師著作

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    Particle swarm optimization incorporating simplex search and center particle for global optimization
    (2008-06-27) Chen-Chien Hsu; Chun-Hwui Gao
    This paper proposes a hybrid approach incorporating an enhanced Nelder-Mead simplex search scheme into a particle swarm optimization (PSO) with the use of a center particle in a swarm for effectively solving multi-dimensional optimization problems. Because of the strength of PSO in performing exploration search and NM simplex search in exploitation search, in addition to the help of a center particle residing closest to the optimum during the optimization process, both convergence rate and accuracy of the proposed optimization algorithm can be significantly improved. To show the effectiveness of the proposed approach, 18 benchmark functions will be adopted for optimization via the proposed approach in comparison to existing methods.
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    Digital redesign of uncertain interval systems via a hybrid particle swarm optimizer
    (Inderscience, 2008-07-01) Chen-Chien Hsu; Chun-Hwei Kao
    In this paper, a hybrid optimiser incorporating particle swarm optimisation (PSO) and an enhanced NM simplex search method is proposed to derive an optimal digital controller for uncertain interval systems having an interval plant based on frequency-response resemblance. By combining the uncertain plant and controller, extremal systems for the redesigned digital system and its continuous counterpart can be obtained as the basis for comparison. The design problem is then formulated as an optimisation problem of an aggregated error function in terms of frequency-response deviation between the redesigned digital system and its continuous counterpart, and subsequently optimised by the proposed optimiser to obtain an optimal set of parameters for the digital controller. Thanks to the performance of the proposed hybrid optimiser, frequency envelope of the redesigned digital system using the digital controller evolutionarily derived by the proposed approach bare a better resemblance to its continuous-time counterpart in comparison to those obtained using existing open-loop discretisation methods.
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    Digital redesign of uncertain interval systems based on extremal gain/phase margins via a hybrid particle swarm optimizer
    (Elsevier, 2010-03-01) Chen-Chien Hsu; Wern-Yarng Shieh; Chun-Hwei Kao
    In this paper, a hybrid optimizer incorporating particle swarm optimization (PSO) and an enhanced NM simplex search method is proposed to derive an optimal digital controller for uncertain interval systems based on resemblance of extremal gain/phase margins (GM/PM). By combining the uncertain plant and controller, extremal GM/PM of the redesigned digital system and its continuous counterpart can be obtained as the basis for comparison. The design problem is then formulated as an optimization problem of an aggregated error function in terms of deviation on extremal GM/PM between the redesigned digital system having an interval plant and its continuous counterpart, and subsequently optimized by the proposed optimizer to obtain an optimal set of parameters for the digital controller. Thanks to the performance of the proposed hybrid optimizer, frequency-response performances of the redesigned digital system using the digital controller evolutionarily derived by the proposed approach bare a far better resemblance to its continuous-time counter part in comparison to those obtained using existing open-loop discretization methods.