Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems

In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

复杂流程工业系统的优化综合与柔性分析

MINLP模型在优化综合与柔性分析中起着重要作用 ,具有两种表示方法 :代数法和逻辑法 ,后者在模型表达与求解方面有很多优点。MINLP模型中还可以集成启发性知识 ,也就是工程经验 ,以加快求解速度 ,并使结果更加合理。对于凸的MINLP问题 ,目前比较成熟的算法有分支界定法、外近似法和广义Bender函数法。对于非凸MINLP问题 ,其求解算法目前尚在研究中 ,已提出的有罚函数法、αBB算法和符号重建算法。此外 ,一些基于随机搜索的方法也得到了应用 ,并且在实践中取得了较好的结果。