基于自适应填充准则的昂贵约束优化算法An Adaptive Infill Criterion Based Expensive Constrained Optimization Algorithm
谭瑛,常圣方,孙超利,李晓波
摘要(Abstract)：
近年来，代理模型辅助的优化算法用于求解昂贵约束问题逐渐受到关注。这类算法中，选择个体进行真实昂贵约束和目标函数计算的策略将直接影响算法的求解结果。但目前的算法对模型更新操作不够严谨，为了解决在昂贵评价次数有限的情况下获得较好的可行解。该算法提出了利用可行规则法进行环境选择，并根据到目前为止是否找到可行解提出一种自适应的填充准则。该方法填充准则思路是：当样本库没有找到可行解时，利用可行性概率选择概率最大的个体进行真实评价。否则若至少找到一个可行解时，选择约束期望提高值最大的个体进行真实的昂贵目标函数和约束函数评价。该方法提高了高斯过程模型的准确度和进化种群的收敛能力。在实验对比上，10个测试函数和工字梁设计优化问题上的运行结果表明，该算法相比于已有针对昂贵约束优化问题的求解算法具有更好的寻优能力。
关键词(KeyWords)： 代理模型;约束优化;可行性概率;约束期望提高
基金项目(Foundation): 国家自然科学基金(61876123);; 山西省自然科学基金(201901D111262)
作者(Author): 谭瑛,常圣方,孙超利,李晓波
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