Although the Efficient Global Optimization (EGO) algorithm has been widely used in multi-disciplinary optimization, it is still difficult to handle multiple constraint problems. In this study, to increase the accuracy of approximation, the Least Squares Support Vector Regression (LSSVR) is suggested to replace the kriging model for approximating both objective and constrained functions while the variances of these surrogate models are still obtained by kriging. To enhance the ability to search the feasible region, two criteria are suggested. First, a Maximize Probability of Feasibility (MPF) strategy to handle the infeasible initial sample points is suggested to generate feasible points. Second, a Multi-Constraint Parallel (MCP) criterion is suggested for multiple constraints handling, parallel computation and validation, respectively. To illustrate the efficiency of the suggested EGO-based method, several deterministic benchmarks are tested and the suggested methods demonstrate a superior performance compared with two other constrained algorithms. Finally, the suggested algorithm is successfully utilized to optimize the fiber path of variable-stiffness beam and lightweight B-pillar to demonstrate the performance for engineering applications.

中文翻译：

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高效全局优化中的约束处理

尽管高效全局优化（EGO）算法已广泛应用于多学科优化，但仍难以处理多约束问题。在本研究中，为了提高逼近的准确性，建议使用最小二乘支持向量回归 (LSSVR) 代替克里金模型来逼近目标函数和约束函数，而这些代理模型的方差仍然通过克里金获得。为了增强搜索可行区域的能力，提出了两个标准。首先，建议采用最大化可行性（MPF）策略来处理不可行的初始样本点，以生成可行点。其次，针对多约束处理、并行计算和验证分别提出了多约束并行 (MCP) 标准。为了说明建议的基于 EGO 的方法的效率，测试了几个确定性基准，并且与其他两种约束算法相比，建议的方法表现出优越的性能。最后，该算法成功地用于优化变刚度梁和轻质B柱的纤维路径，以展示工程应用的性能。