多任务优化算法及应用研究综述

Research review of multitasking optimization algorithms and applications

进化多任务优化(EMTO)是进化计算中一种新型方法,它可以同时解决多个相关的优化任务,并通过任务之间的知识转移增强每个任务的优化。近年来,越来越多的进化多任务优化相关研究致力于利用它强大的并行搜索能力和降低计算成本的潜力优化各种问题,并且EMTO已应用于各种各样的实际场景当中。从EMTO的原理、核心设计、应用以及挑战四个方面对EMTO的算法及应用进行了讨论。首先介绍了EMTO的大致分类,分别从两个层次、四个方面介绍,包括单种群多任务、多种群多任务、辅助任务形式以及多形式任务形式;其次介绍EMTO的核心组件设计,包括任务构建以及知识转移;最后对它的各种应用场景进行介绍,并对今后研究做了总结与展望。

Evolutionary MultiTasking Optimization(EMTO)is one of the new methods in evolutionary computing,which can simultaneously solve multiple related optimization tasks and enhance the optimization of each task through knowledge transfer between tasks.In recent years,more and more research on evolutionary multitasking optimization has been devoted to utilizing its powerful parallel search capability and potential for reducing computational costs to optimize various problems,and EMTO has been used in a variety of real-world scenarios.The researches and applications of EMTO were discussed from four aspects:principle,core design,applications,and challenges.Firstly,the general classification of EMTO was introduced from two levels and four aspects,including single-population multitasking,multi-population multitasking,auxiliary task,and multiform task.Next,the core component design of EMTO was introduced,including task construction and knowledge transfer.Finally,its various application scenarios were introduced and a summary and outlook for future research was provided.