基于增强生长型神经气的高维多目标进化算法  

Enhanced Growing Neural Gas Based Many-Objective Evolutionary Algorithm

作　　者：薛明 王鹏 童向荣 XUE Ming;WANG Peng;TONG Xiangrong(School of Computer and Control Engineering,Yantai University,Yantai 264005,China)

机构地区：[1]烟台大学计算机与控制工程学院,烟台264005

出　　处：《数据采集与处理》2024年第3期634-648,共15页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(62072392,61972360,62103350);山东省重大科技创新工程项目(2019522Y020131);山东省自然科学基金(ZR2020QF113,ZR2020QF046);烟台市重点实验室:高端海洋工程装备智能技术。

摘　　要：随着对高维多目标优化问题的深入研究,带有不规则Pareto前沿的高维多目标优化问题因其复杂的Pareto前沿分布,给现有方法的求解带来了挑战。针对上述问题,提出一种基于增强生长型神经气的高维多目标进化算法,该算法综合生长型神经气网络的学习特性与二元质量指标的优化特性来增强种群在不规则Pareto前沿的收敛压力。首先,设计了一种增强的生长型神经气网络,该网络利用Pareto最优前沿的拓扑信息指导种群向Pareto最优前沿方向收敛。然后,提出了一种联合度量指标以配合Pareto支配信息来综合评价个体的收敛性。最后,提出一种基于自适应参考点的环境选择增强种群在高维目标空间的多样性。为验证所提算法的性能,在DTLZ和WFG基准问题集中的44个不规则高维多目标优化问题与5种先进的高维多目标进化算法进行对比实验。实验结果表明,所提出的基于增强生长型神经气的高维多目标进化算法的整体性能优于对比算法。With the in-depth research on many-objective optimization problems,many-objective optimization problems with irregular Pareto frontiers pose challenges to existing methods due to their complex Pareto frontiers distribution.To address the above issues,a many-objective evolutionary algorithm based on the enhanced growing neural gas is proposed.This algorithm combines the learning characteristics of growing neural networks with the optimization characteristics of binary quality indicators to enhance the convergence pressure of the population at the irregular Pareto frontier.Firstly,an enhanced growing type of neural gas network is designed,which utilizes the topological information of the Pareto optimal frontier to guide the population to converge towards the Pareto optimal frontier direction.Then,a joint metric is proposed to comprehensively evaluate the convergence of individuals in conjunction with Pareto dominance information.Finally,an adaptive reference point based environment selection is proposed to enhance the diversity of the population in high-dimensional target space.To verify the performance of the proposed algorithm,44 irregular many-objective optimization problems in the DTLZ and WFG benchmark problem sets are compared with five advanced many-objective evolutionary algorithms.Experimental results show that the overall performance of the proposed many-objective evolutionary algorithm based on enhanced growing neural gas is superior to the comparison algorithms.

关 键 词：多目标优化 多目标进化算法 度量指标 不规则Pareto前沿 生长型神经气 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]