动态多目标优化进化算法研究进展

马永杰; 陈敏; 龚影; 程时升; 王甄延

doi:10.16383/j.aas.c190489

动态多目标优化进化算法研究进展

doi: 10.16383/j.aas.c190489

马永杰^1,,
陈敏^1,,
龚影^1,,
程时升^1,,
王甄延^1,

1. 西北师范大学物理与电子工程学院兰州 730070

基金项目: 国家自然科学基金(62066041, 41861047)资助

详细信息

作者简介:
马永杰：西北师范大学物理与电子工程学院电子系教授. 主要研究方向为进化算法. 本文通信作者. E-mail: myjmyj@nwnu.edu.cn

陈敏：西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为进化算法. E-mail: cm9690@126.com

龚影：西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为智能计算. E-mail: 15320834175@163.com

程时升：西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为智能计算. E-mail: shishengcss@163.com

王甄延：西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为智能计算. E-mail: wzy1136390111@163.com

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出版历程
- 收稿日期: 2019-06-26
- 录用日期: 2019-11-15
- 刊出日期: 2020-11-24

Research Progress of Dynamic Multi-objective Optimization Evolutionary Algorithm

1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070

Funds: Supported by National Natural Science Foundation of China (62066041, 41861047)

摘要

摘要: 动态多目标优化问题(Dynamic multi-objective optimization problems, DMOPs)已成为工程优化的研究热点, 其目标函数, 约束函数和相关参数都可能随时间不断变化, 如何利用搜索到的历史最优解对新的环境变化做出快速响应, 是设计动态多目标优化进化算法(Dynamic multi-objective optimization evolutionary algorithm, DMOEA)的重点和难点. 本文在介绍DMOEA的基础上, 分析了近年来基于个体和种群级别的环境响应策略, 多策略混合等的DMOEA主要研究进展, 并介绍了DMOEA的性能测试函数, 评价指标以及在工程优化领域中的应用, 分析了DMOEA研究中仍面临的主要问题, 展望了未来的研究方向.
- 动态优化 /
- 多目标优化 /
- 环境响应策略 /
- 进化算法
Abstract: Dynamic multi-objective optimization problems (DMOPs) have become a research focus on the engineering optimization, the objective function, constraint functions and related parameters are likely to be changing over time, How to make rapid response to new environment changes by using the historical optimal solution is the key and difficulty of designing dynamic multi-objective optimization evolutionary algorithm (DMOEA). Based on the introduction to DMOEA, this paper analyzes the main research progress of DMOEA based on individual and population level environmental response strategy and multi-strategy mixing in recent years, introduces the performance test function, evaluation index and application of DMOEA in the field of engineering optimization, analyzes the main problems still faced in DMOEA research and giving an outlook to the future research.
- Dynamic optimization /
- multi-objective optimization /
- environmental response strategy /
- evolutionary algorithm

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图 1 DMOEA的设计流程框图

Fig. 1 The design flow chart of DMOEA

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图 2 基于卡尔曼滤波的预测模型图

Fig. 2 Relationship of EA with KF model

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图 3 动态环境中多种群调度方法的框架

Fig. 3 Framework for multi-population methods with scheduling in dynamic environments

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图 4 BSCA算法体系结构图

Fig. 4 The architecture of BSCA inspired by human NEI systems

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图 5 多种群的粒子群优化框架示意图

Fig. 5 The framework of multi-swarm particle swarm optimization

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图 6 动态进化环境模型框图

Fig. 6 A general framework of dynamic environment evolutionary model

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