We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mec...
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We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mechanism to progressively relax or tighten the constraint, The relaxation or tightening of constraint guides the GA to the subspace for further iteration. The system referred to as the iterative GA learning module is useful for learning an efficient fuzzy control algorithm based on a predefined linguistic terms set. The overall approach was applied to learn a fuzzy algorithm for a water bath temperature control. The simulation results demonstrate the effectiveness of the approach in automating an industrial process.
An approach that combines geneticalgorithm (GA) and control vector parameterization (CVP) is proposed to solve the dynamic optimization problems of chemical processes using numerical methods. In the new CVP method, c...
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An approach that combines geneticalgorithm (GA) and control vector parameterization (CVP) is proposed to solve the dynamic optimization problems of chemical processes using numerical methods. In the new CVP method, control variables are approximated with polynomials based on state variables and time in the entire time interval. The iterative method, which reduces redundant expense and improves computing efficiency, is used with GA to reduce the width of the search region. Constrained dynamic optimization problems are even more difficult. A new method that embeds the information of infeasible chromosomes into the evaluation function is introduced in this study to solve dynamic optimization problems with or without constraint. The results demonstrated the feasibility and robustness of the proposed methods. The proposed algorithm can be regarded as a useful optimization tool, especially when gradient information is not available.
Uncontrolled charging behaviors of electric vehicle may have negative impacts on the operation of the power system. To avoid this, a hierarchical coordinated control strategy based on bi-level programming is proposed....
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ISBN:
(纸本)9781479942398
Uncontrolled charging behaviors of electric vehicle may have negative impacts on the operation of the power system. To avoid this, a hierarchical coordinated control strategy based on bi-level programming is proposed. The upper-level is devoted to minimize the operational cost of several aggregators and the system load variance. On the lower level, each aggregator controls the charging/discharging behaviors of EVs to meet the dispatch scheme determined by the upper decision-maker. An iterative genetic algorithm is employed to solve the problem. Finally, a test system including 3 aggregators is employed to demonstrate the performance of the approach proposed.
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