基于多目标数据生成的昂贵多目标进化算法  

Expensive Multi-Objective Evolutionary Algorithm with Multi-Objective Data Generation

作　　者：黎建宇 詹志辉[1] LI Jian-Yu;ZHAN Zhi-Hui(School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006)

机构地区：[1]华南理工大学计算机科学与工程学院,广州510006

出　　处：《计算机学报》2023年第5期896-908,共13页Chinese Journal of Computers

基　　金：国家自然科学基金面上项目(62176094)资助.

摘　　要：昂贵多目标优化问题是一类需要同时优化多个相互冲突且评估计算成本十分昂贵的目标的复杂优化问题,需要算法在计算资源受限的情况下尽可能找到目标值好且多样性好的一系列非支配解.进化计算方法是求解多目标优化问题的有效手段,但在求解昂贵多目标优化问题时仍面临多样性和收敛性这两个方面的挑战,即难以找到多样性好且收敛到全局最优的一系列解.针对上述挑战,本文提出了新型的基于多目标数据生成的昂贵多目标进化算法.本文的贡献点和创新点主要有以下三个方面.首先,本文提出并证明了非支配解生成定理,并基于此提出了多目标数据生成方法,以更有效地搜索到更多非支配解,提高算法的多样性.其次,本文提出了多种群多代理框架,使用多个代理模型替代评估成本昂贵的真实目标函数,并协同演化多个种群对多个代理模型进行协同求解,从而提高算法的收敛性.再次,基于上述提出的方法和框架,本文提出了基于多目标数据生成的昂贵多目标进化算法,以对昂贵多目标优化问题进行求解.为了验证算法性能,本文在两个著名测试集的共16个问题上进行了丰富的大量测试实验,并与现有的五个前沿算法进行对比.实验结果表明,本文提出的算法能在大部分问题上取得比所有对比算法都更好的性能,具有很好的有效性和高效性.Expensive multi-objective optimization problem is a class of complex optimization problems that involve multiple objectives that are conflicted with each other and computationally expensive,which requires algorithms to find a set of non-dominated solutions as many and diversity as possible with limited computational resources.Although evolutionary computation algorithms are regarded as effective tools for solving multi-objective optimization problems,they still face the diversity and convergence challenges when solving expensive multi-objective optimization problems,i.e.,difficulty to find a set of solutions that are of good diversity and converged to Pareto front.To address the diversity and convergence challenges,this paper proposes a novel multi-objective data generation-based expensive multi-objective evolutionary algorithm.The contributions and innovations of this paper can be mainly summarized in the following three aspects.First,this paper puts forward and proves the non-dominated solution generation theorem,and then proposes a multi-objective data generation method based on the theorem,so as to obtain more non-dominated solutions more efficiently for improving the solution diversity.Second,this paper proposes a multiple population for multiple surrogates framework that co-evolves multiple populations to efficiently optimize multiple surrogates that are built for multiple real expensive objectives respectively.Third,based on the above proposed method and framework,this paper proposes the expensive multi-objective evolutionary algorithm based on multi-objective data generation for efficiently solving the expensive multi-objective optimization problem.To validate the algorithm performance,extensive experimental analyses are conducted on 16 problems from two well-known test sets in the related field with five existing state-of-the-art algorithms as competitors in this paper.The experimental results show that the algorithm proposed in this paper is able to achieve better metric values than all the compared alg

关 键 词：昂贵优化 多目标优化 进化计算 数据生成 协同演化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]