融合指标分组的高维混合多目标进化优化  

Hybrid Many-Objective Evolutionary Optimization Combined with Indexs Decomposition

作　　者：李玲[1] 郭广颂[1] LI Ling;GUO Guangsong(School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China)

机构地区：[1]郑州航空工业管理学院智能工程学院,郑州450046

出　　处：《计算机工程与应用》2023年第4期165-174,共10页Computer Engineering and Applications

基　　金：河南省重点研发与推广专项-科技攻关(212102210491,212102310299)。

摘　　要：高维混合多目标优化问题因包含多个不同类型指标,目前尚缺乏有效求解该问题的进化优化方法。提出一种基于目标分组的高维混合多目标并行进化优化方法。采用深度学习神经网络预测种群隐式性能指标;基于指标相关性,将高维混合多目标优化问题分解为若干子优化问题;采用多种群并行进化算法,求解分解后的每一子优化问题,并基于各子种群的非被占优解构建外部保存集;采用聚合函数对外部保存集个体进一步优化,得到Pareto最优解集。在室内布局优化问题中验证所提方法,实验结果表明,所提方法的Pareto最优解在收敛性、分布性以及延展性等方面均优于对比方法。Because of containing multiple different types of indicators in hybrid many-objective optimization problems,it is lack of effective evolutionary optimization method to solve this problem.This paper proposes a hybrid many-objective coevolutionary optimization method based on objectives decomposition.Firstly,the population implicit performance indicators is predicted by deep learning neural network.Then,the hybrid many-objective optimization problem is decomposed into several sub-problems based on the target correlation.The each sub-problem is solved by parallel evolutionary algorithm using multiple species.The final Pareto set of the optimized many-objective is achieved by archiving those sets of non-dominated solutions coming from the sub-populations.Finally,the Pareto optimal solution set is achieved by optimizing the aggregate function with individuals of those sets of non-dominated solutions coming from the sub-populations.The proposed method is applied to indoor layout optimization problem,the experimental results show that the proposed method is better than the contrast methods in convergence,distribution,extensibility and so on.

关 键 词：进化算法 交互 混合指标 深度学习 指标分组 高维 多目标 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]