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作 者:谭瑛[1] 常圣方 孙超利[1] 李晓波 TAN Ying;CHANG Sheng-fang;SUN Chao-li;LI Xiao-bo(School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China)
机构地区:[1]太原科技大学计算机科学与技术学院,太原030024
出 处:《太原科技大学学报》2024年第4期329-335,共7页Journal of Taiyuan University of Science and Technology
基 金:国家自然科学基金(61876123);山西省自然科学基金(201901D111262)。
摘 要: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。In recent years,agent model assisted optimization algorithms for solving expensive constraint problems have attracted more and more attention.In this kind of algorithm,the strategy of selecting individuals for real constraints and objective function calculation will directly affect the solution results of the algorithm.However,the current algorithm is not rigorous enough to update the model.In order to obtain a better feasible solution when the number of expensive evaluations is limited.In this algorithm,the feasible rule method is used for environment selection,and an adaptive filling criterion is proposed according to whether the feasible solution is found so far.The filling criterion of this method is:when the sample database does not find a feasible solution,use the feasibility probability to select the individual with the greatest probability for real evaluation.Otherwise,if at least one feasible solution is found,the individual with the largest constraint expectation improvement is selected to evaluate the real expensive objective function and constraint function.This method improves the accuracy of Gaussian process model and the convergence ability of evolutionary population.In the experimental comparison,the operation results of 10 test functions and I-beam design optimization problems show that the algorithm has better optimization ability than the existing algorithms for expensive constrained optimization problems.
分 类 号:TP18[自动化与计算机技术—控制理论与控制工程]
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