Memetic Computing最新文献_第7页

Thematic issue on knowledge and data driven evolutionary multi-objective optimization 知识和数据驱动的进化多目标优化专题

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-04-19 DOI: 10.1007/s12293-022-00369-6

Ran Cheng, Jinliang Ding, Wenli Du, Yaochu Jin

引用次数: 3

Constrained Multi-Objective Optimization with a Limited Budget of Function Evaluations 函数评估预算有限的约束多目标优化

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-04-08 DOI: 10.1007/s12293-022-00363-y

R. de Winter, Philip Bronkhorst, Bas van Stein, Thomas Bäck

引用次数: 8

Handling constrained multi-objective optimization problems with heterogeneous evaluation times: proof-of-principle results 处理具有异构评估时间的约束多目标优化问题：原理结果证明

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-03-07 DOI: 10.1007/s12293-022-00362-z

Julian Blank, K. Deb

引用次数: 7

CCMBO: a covariance-based clustered monarch butterfly algorithm for optimization problems CCMBO:一种基于协方差的聚类帝王蝶优化算法

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-03-05 DOI: 10.1007/s12293-022-00359-8

S. Yazdani, E. Hadavandi, Mohammad Mirzaei

引用次数: 1

Guest editorial 客座编辑

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-02-24 DOI: 10.1007/s12293-022-00361-0

Ya-wen Hou, Yuan Yuan, Zexuan Zhu

引用次数: 0

Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid 基于偏好的多目标强化学习智能电网多微网系统优化问题

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-02-22 DOI: 10.1007/s12293-022-00357-w

Jiangjiao Xu, Ke Li, Mohammad Abusara

{"title":"Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid","authors":"Jiangjiao Xu, Ke Li, Mohammad Abusara","doi":"10.1007/s12293-022-00357-w","DOIUrl":"https://doi.org/10.1007/s12293-022-00357-w","url":null,"abstract":"<p>Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid system is a big challenge. To design a multi-microgrid power system, an intelligent multi-microgrids energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques. The power system model can be divided into three layers: the consumer layer, the independent system operator layer, and the power grid layer. Each layer intends to maximize its benefit. The PMORL is proposed to lead to a Pareto optimal set for each object to achieve these objectives. A non-dominated solution is decided to execute a balanced plan not to favor any particular participant. The preference-based results show that the proposed method can effectively learn different preferences. The simulation outcomes confirm the performance of the PMORL and verify the viability of the proposed method.</p>","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"1 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

The design of evolutionary feature selection operator for the micro-expression recognition 用于微表情识别的进化特征选择算子的设计

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-02-18 DOI: 10.1007/s12293-021-00350-9

Zhan WangPing, Jiang Min, Yao JunFeng, Liu KunHong, Wu QingQiang

引用次数: 3

A constrained multi-objective optimization algorithm with two cooperative populations 两个合作种群的约束多目标优化算法

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-02-08 DOI: 10.1007/s12293-022-00360-1

Jianlin Zhang, Jie Cao, Fuqing Zhao, Zuohan Chen

引用次数: 3

Solving large-scale multiobjective optimization via the probabilistic prediction model 利用概率预测模型求解大规模多目标优化问题

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-01-31 DOI: 10.1007/s12293-022-00358-9

Haokai Hong, Kai Ye, Min Jiang, Donglin Cao, Kay Chen Tan

引用次数: 2

A particle swarm optimization based multiobjective memetic algorithm for high-dimensional feature selection 基于粒子群优化的多目标模因算法用于高维特征选择

IF 4.7 2区计算机科学

Memetic Computing Pub Date : 2022-01-29 DOI: 10.1007/s12293-022-00354-z

Juanjuan Luo, Dongqing Zhou, Lingling Jiang, Huadong Ma

{"title":"A particle swarm optimization based multiobjective memetic algorithm for high-dimensional feature selection","authors":"Juanjuan Luo, Dongqing Zhou, Lingling Jiang, Huadong Ma","doi":"10.1007/s12293-022-00354-z","DOIUrl":"https://doi.org/10.1007/s12293-022-00354-z","url":null,"abstract":"<p>Feature selection, as one of the dimension reduction methods, is a crucial processing step in dealing with high-dimensional data. It tries to preserve feature subset representing the whole feature space, which aims to reduce redundancy and increase the classification accuracy. Since the two objectives are usually in conflict with each other, feature selection is modeled as a multi-objective problem. However, the high search space and discrete Pareto front makes it not easy for existing evolutionary multiobjective algorithms. Classic evolutionary computation method, which is often applied to feature selection problem straightforwardly, gradually exposes its inefficiency in searching process. Hence, a particle swarm optimization based multiobjective memetic algorithm for high-dimensional feature selection is designed in this paper to deal with above shortcomings. Its basic idea is to model feature selection as a multiobjective optimization problem by optimizing the number of features and the classification accuracy in supervised condition simultaneously, in which information entropy based initialization and adaptive local search are designed to improve the search efficiency. Moreover, a new particle velocity update rule considering both convergence and diversity of solutions is designed to update particles, and a fast discrete nondominated sorting strategy is designed to rank the Pareto solutions. These strategies enable the proposed algorithm to gain better performance on both the quality and size of feature subset. The experimental results show that the proposed algorithm can improve the quality of Pareto fronts evolved by the state-of-the-art algorithms for feature selection.</p>","PeriodicalId":48780,"journal":{"name":"Memetic Computing","volume":"33 4","pages":""},"PeriodicalIF":4.7,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10