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首页> 外文会议>2014 IEEE International Conference on System Science ang Engineering >A hybrid differential evolution algorithm with invasive weed optimization and its application to modeling of carbon content
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A hybrid differential evolution algorithm with invasive weed optimization and its application to modeling of carbon content

机译：具有入侵杂草优化的混合差分进化算法及其在碳含量建模中的应用

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This paper aims to the prediction of carbon content in spent catalyst in a continuous catalytic reforming (CCR) plant based on least squares vector machines (LSSVM). When modeling by LSSVM, the problem of optimizing the hyper-parameters draws many researchers' attention. In this paper, a novel hybrid algorithm named IWODE is proposed to deal with it. The algorithm embeds invasive weed optimization (IWO) as a local refinement procedure into differential evolution with adaptive crossover rate. New competitive exclusion and adaptive step length of spatial dispersal based on individuals' distance are introduced to make IWO more suitable as a local search algorithm. Simulation results and comparisons based on some well-known benchmarks indicate the efficiency of IWODE. And the predicted results of carbon content using the proposed method agree with the actual values well. The method is compared with five other techniques, including LSSVM optimized by DE, IWO, other two modified versions of DE and back propagation neural network (BPNN). The obtained results demonstrate that the proposed IWODE-LSSVM is superior to others in generalization performance and prediction ability.
机译：本文旨在基于最小二乘向量机（LSSVM）预测连续催化重整（CCR）厂中废催化剂中的碳含量。当通过LSSVM进行建模时，优化超参数的问题引起了许多研究人员的关注。本文提出了一种新的混合算法IWODE。该算法将侵入性杂草优化（IWO）作为局部优化过程嵌入具有自适应交叉速率的差分进化中。引入了新的竞争排斥和基于个体距离的空间扩散自适应步长，使IWO更适合作为局部搜索算法。基于一些知名基准的仿真结果和比较表明了IWODE的效率。所提方法对碳含量的预测结果与实际值吻合良好。该方法与其他五种技术进行了比较，包括通过DE，IWO优化的LSSVM，DE的其他两个修改版本以及反向传播神经网络（BPNN）。所得结果表明，所提出的IWODE-LSSVM在泛化性能和预测能力上均优于其他方法。

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