9,607 research outputs found

    Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models

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    It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework. This framework adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. For the single dimensional sub-problems obtained through decomposition, accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding sub-problems directly. As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models. By these means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional CC algorithms and a non-CC algorithm even with much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974

    Fast design optimization of UWB antenna with WLAN Band-Notch

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    In this paper, a methodology for rapid design optimization of an ultra-wideband ( UWB) monopole antenna with a lower WLAN band-notch is presented. The band-notch is realized using an open loop resonator implemented in the radiation patch of the antenna. Design optimization is a two stage process, with the first stage focused on the design of the antenna itself, and the second stage aiming at identification of the appropriate dimensions of the resonator with the purpose of allocating the band-notch in the desired frequency range. Both optimization stages are realized using surrogate-based optimization involving variable-fidelity electromagnetic ( EM) simulation models as well as an additive response correction ( first stage), and sequential approximate optimization ( second stage). The final antenna design is obtained at the CPU cost corresponding to only 23 high-fidelity EM antenna simulations
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