Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization

强化学习 计算机科学 差异进化 水准点(测量) 进化算法 人工智能 趋同(经济学) 神经进化 进化计算 人工神经网络 机器学习 人口 灵活性(工程) 概括性 数学优化 数学 心理学 统计 人口学 大地测量学 社会学 地理 经济 心理治疗师 经济增长
作者
Zhenzhen Hu,Wenyin Gong,Witold Pedrycz,Yanchi Li
出处
期刊:Swarm and evolutionary computation [Elsevier BV]
卷期号:83: 101387-101387 被引量:25
标识
DOI:10.1016/j.swevo.2023.101387
摘要

Solving constrained optimization problems (COPs) with evolutionary algorithms (EAs) is a popular research direction due to its potential and diverse applications. One of the key issues in solving COPs is the choice of constraint handling techniques (CHTs), as different CHTs can lead to different evolutionary directions. Combining EAs with deep reinforcement learning (DRL) is a promising and emerging approach for solving COPs. Although DRL can help solve the problem of pre-setting operators in EAs, neural networks need to obtain diverse training data within a limited number of evaluations in EAs. Based on the above considerations, this work proposes a DRL assisted co-evolutionary differential evolution, named CEDE-DRL, which can effectively use DRL to help EAs solve COPs. (1) This method incorporates co-evolution into the extraction of training data for the first time, ensuring the diversity of samples and improving the accuracy of the neural network model through information exchange between multiple populations. (2) Multiple CHTs are used for offspring selection to ensure the algorithm's generality and flexibility. (3) DRL is used to evaluate the population state, taking into account feasibility, convergence, and diversity in the state setting and using the overall degree of improvement as a reward. The neural network selects suitable parent populations and corresponding archives for mutation. Finally, (4) to avoid premature convergence and local optima, an adaptive operator selection and individual archive elimination mechanism is added. Comparisons with state-of-the-art algorithms on benchmark functions CEC2010 and CEC2017 show that the proposed method performs competitively and produced robust solutions. The results of the application test set CEC2020 show that the proposed algorithm is also effective in real-world problems.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小马甲应助香蕉采纳,获得10
刚刚
DyG完成签到,获得积分10
刚刚
高序发布了新的文献求助10
1秒前
2秒前
2秒前
yan完成签到,获得积分10
2秒前
菡菡菡菡菡完成签到,获得积分10
2秒前
honey完成签到,获得积分10
2秒前
天天快乐应助檀熹采纳,获得10
3秒前
沉积岩完成签到,获得积分10
3秒前
沧浪江发布了新的文献求助10
3秒前
汉堡包应助蓝桉采纳,获得10
3秒前
silstorm完成签到,获得积分10
4秒前
金东华完成签到,获得积分10
4秒前
妮妮爱smile完成签到,获得积分10
4秒前
南有乔木发布了新的文献求助10
4秒前
4秒前
zly完成签到,获得积分10
4秒前
失眠夏山完成签到,获得积分10
5秒前
junfeiwang发布了新的文献求助10
5秒前
5秒前
背后梦安完成签到,获得积分10
5秒前
6秒前
chen完成签到,获得积分10
7秒前
7秒前
7秒前
嗒嗒完成签到,获得积分10
8秒前
失眠的耳机完成签到,获得积分10
8秒前
8秒前
9秒前
关显锋发布了新的文献求助10
9秒前
champion完成签到,获得积分10
10秒前
10秒前
Owen应助包容的水壶采纳,获得10
10秒前
WANG给WANG的求助进行了留言
11秒前
善良天抒发布了新的文献求助10
11秒前
LLC发布了新的文献求助10
11秒前
huangjie发布了新的文献求助10
12秒前
星辰大海应助junfeiwang采纳,获得10
12秒前
ergrsbf发布了新的文献求助10
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969033
求助须知:如何正确求助?哪些是违规求助? 3513900
关于积分的说明 11170818
捐赠科研通 3249256
什么是DOI,文献DOI怎么找? 1794708
邀请新用户注册赠送积分活动 875326
科研通“疑难数据库(出版商)”最低求助积分说明 804759