基于Kriging模型和两目标约束应对策略的代理优化算法

Surrogate-based optimization algorithm based on Kriging models and bi-objective constraint-handling strategy

为了解决包含黑箱约束的复杂工程系统的优化设计问题,提出一种基于Kriging模型和两目标约束应对策略的代理优化算法。该算法将改进目标函数的期望改进准则和刻画可行域边界的可行性概率准则同时作为优化目标,再从得到的Pareto集中选取新试验点,不仅提高了新试验点选取的目的性也使新试验点兼具探索最优解和开发可行域边界的能力。最后,通过两个数学算例和一个工程算例将所提算法与已有算法进行比较,计算结果表明基于两目标约束应对策略的代理优化算法具有更高的优化精度、效率和稳健性。

To solve the design optimization problems of complex engineering systems with black-box constraints,a surrogate-based optimization algorithm was proposed based on Kriging models and bi-objective constraint-handling strategy.By taking the expected improvement criterion of improving the objective function and the feasibility probability criterion of characterizing the feasible region boundary as objectives simultaneously,the new trials from Pareto sets purposefully was selected so as to make the new added trials have the ability to balance optimal solution exploration and feasible region boundary exploitation.The proposed algorithm was tested on two numerical and one engineering benchmarks and was compared with the existed algorithms.The numerical results indicated that the proposed algorithm based on the bi-objective constraint-handling strategy was more accurate,efficient and robust.