基于自适应搜索策略的约束多目标优化算法  

Constrained Multiobjective Optimization Algorithm Based on Adaptive Search Strategy

作　　者：姚堂旭 姚杰愉 鲁凯琳 朱一平 袁鑫 YAO Tangxu;YAO Jieyu;LU Kailin;ZHU Yiping;YUAN Xin(School of Information Engineering,Huzhou University,Huzhou Zhejiang 313000,China)

机构地区：[1]湖州师范学院信息工程学院,浙江湖州313000

出　　处：《信息与电脑》2024年第1期40-42,共3页Information & Computer

摘　　要：平衡目标函数和约束条件是现有约束多目标优化算法面临的共同难题。为了解决这个难题,文章提出了一种基于自适应搜索策略的约束多目标优化算法(ASSCMO)。为验证ASSCMO的性能,将其与3种优秀的约束多目标优化算法在两组基准测试集上进行仿真实验。实验结果表明,ASSCMO在求解约束多目标优化问题上更具有竞争力。The balance between the objective function and constrained conditions is a common challenge faced by existing constrained multiobjective optimization algorithms.To address this issue,a constrained multiobjective optimization algorithm based on adaptive search strategy(ASSCMO)is proposed.To validate the performance of ASSCMO,it is compared with three excellent constrained multiobjective optimization algorithms through simulation experiments on two sets of benchmark test suites.The experimental results indicate that ASSCMO is more competitive in solving constrained multiobjective problems.

关 键 词：约束多目标优化 自适应搜索 仿真实验 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]