Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231000079-11.doi: 10.11896/jsjkx.231000079

• Intelligent Computing • Previous Articles     Next Articles

Dynamic Multi-Objective Optimization Algorithm with Irregularly Varying Number of Objectives

LI Sanyi, LIU Shuang   

  1. Zhengzhou University of Light Industry,Zhengzhou 450000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:LI Sanyi,born in 1988,Ph.D,lecturer.His main research interests include multi-objective optimization,process automation in process industry,neural network modeling and fault diagnosis of power system equipment.
  • Supported by:
    National Natural Science Foundation of China(62203402,62103378) and Science and Technology Project of Henan Province(202102310284,232102321034).

PDF (PC)

171

Abstract: tIn this paper,a hybrid strategy based initial population prediction algorithm(HIPPA) is proposed to solve the dynamic multi-objective optimization problem where the number of objectives varies irregularly with time.HIPPA determines whether the environment has changed according to the number of objectives,and divides the environment type according to the different number of objectives.In the population initialization stage,the initial population is generated by three mechanisms.First,an improved neural network algorithm is trained using historical population information to generate a part of the initial population.Second,the improved elite strategy uses historical population information to generate a portion of the initial population.Finally,an improved random strategy is used to generate a portion of the population to maintain the diversity of the population.In this paper,the effectiveness of the proposed algorithm is verified by reference experiment F1-F5,and the results are compared with other dynamic optimization algorithms.Experimental results show that HIPPA can more effectively solve the dynamic multi-objective optimization problem where the number of objectives varies irregularly with time.

Key words: Dynamic multi-objective optimization, Neural network, Prediction, The number of objectives varies irregularly

CLC Number: 

  • TP301
[1]CHEN J H,CHENG C W.Multi-objective evolutionary optimization of dynamic service facility location problems[J/OL].https://doi.org/10.1109/SECON.2011.5752961.
[2]DEB K,BHASKARA RAO U,KARTHIK S.Dynamic multi-objective optimization and decision-making using modified NSGA-II:A case study on hydro-thermal power scheduling[J/OL].https://doi.org/10.1007/978-3-540-70928-2_60.
[3]HUTZSCHENREUTER A K,BOSMAN P A N,POUTRÉ H L.Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management[J/OL].https://doi.org/10.1007/978-3-642-01020-0_27.
[4]OU J W,ZHENG J H,RUAN G,et al.A pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization[J].Applied Soft Computing,2019,85:105673.
[5]LIU M,ZHENG J H,WANG J N,et al.An Adaptive Diversity Introduction Method for Dynamic Evolutionary Multiobjective Optimization[J/OL].https://doi.org/10.1109/CEC.2014.6900364.
[6]GOH C K,TAN K C.A competitive-cooperative coevolutionaryparadigm for dynamic multiobjective optimization[J].IEEE Transactions on Evolutionary Computation,2009,13(1):103-127.
[7]LI J X,LIU R C,WANG R N.A change type-based self-adaptive response strategy for dynamicmulti-objective optimization[J].Knowledge-Based Systems,2022,243:108447.
[8]WANG C F,YEN G G,ZOU F.A novel predictive methodbased on key points for dynamic multi-objective optimization[J].Expert Systems with Applications,2022,190:116127.
[9]WANG Y,LI K C,WANG G G.Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization[J].Mathematics,2022,10(12):2117.
[10]OU J W,LI M G,XING L N,et al.Individual-based self-lear-ning prediction method for dynamic multi-objective optimization[J].Information Sciences,2022,613:401-418.
[11]YAN L,QI W L,LIANG J,et al.Inter-individual correlation and Dimension Based Dual Learning for Dynamic Multi-objective Optimization[J/OL].https://doi.org/10.1109/TEVC.2023.3235196.
[12]KAMALI S R,BANIROSTAM T,MOTAMENI H,et al.An immune inspired multi-agent system for dynamic multi-objective optimization[J].Knowledge-Based Systems,2023,262:110242.
[13]XU X X,TAN Y Y,ZHENG W,et al.Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition[J].Applied Sciences,2018,8(9):1673.
[14]YANG Y,LIAO Q F,WANG J,et al.Application of multi-objective particle swarm optimization based on short-term memory and K-means clustering in multi-modal multi-objective optimization[J].Engineering Applications of Artificial Intelligence,2022,112:104866.
[15]KARABADJI N E I,BELDJOUDI S,SERIDI H,et al.Improving memory-based user collaborative filtering with evolutionary multi-objective optimization[J].Expert Systems with Applications,2018,98:153-165.
[16]ZHUO J L,CHAKRABARTI C.An efficient dynamic taskscheduling algorithm for battery powered DVS systems[C]//ASP-DAC '05.2005:846-849.
[17]CHEN R Z,LI K,YAO X.Dynamic Multi-Objectives Optimization with a Changing Number of Objectives[J].IEEE Transactions on Evolutionary Computation,2018,22:157-171.
[18]ISMAYILOV G,TOPCUOGLU H R.Neural network basedmulti-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing[J].Future Generation Computer Systems,2020,102:307-322.
[19]JIANG Y,ZHENG Y P,ZHANG X,et al.Motor AbnormalSound Diagnosis Based on Improved BP Neural Network[J].Journal of Chongqing University of Technology,2020,34(1):242-246,262.
[20]ZENG G Q,CHEN J,LI L M,et al.An improved multi-objective population-based extremal optimization algorithm with polynomial mutation[J].Information Sciences,2016,330:49-73.
[21]DEB K,THIELE L,LAUMANNS M,et al.Scalable test pro-blems for evolutionary multiobjective optimization[J].Evolutio-nary Multiobjective Optimization,2005:105-145.
[22]ALLMENDINGER R,JASZKIEWICZ A,LIEFOOGHE A,et al.What if we increase the number of objectives?Theoretical and empirical implications for many-objective combinatorial optimization[J].Computers & Operations Research,2022,145:105857.
[1] LIU Renyu, CHEN Xin, SHANG Honghui, ZHANG Yunquan. Optimization of Atomic Kinetics Monte Carlo Program TensorKMC Based on Machine Learning Atomic Potential Functions [J]. Computer Science, 2024, 51(9): 23-30.
[2] ZHU Fukun, TENG Zhen, SHAO Wenze, GE Qi, SUN Yubao. Semantic-guided Neural Network Critical Data Routing Path [J]. Computer Science, 2024, 51(9): 155-161.
[3] LI Zhe, LIU Yiyang, WANG Ke, YANG Jie, LI Yafei, XU Mingliang. Real-time Prediction Model of Carrier Aircraft Landing Trajectory Based on Stagewise Autoencoders and Attention Mechanism [J]. Computer Science, 2024, 51(9): 273-282.
[4] TANG Ying, WANG Baohui. Study on SSL/TLS Encrypted Malicious Traffic Detection Algorithm Based on Graph Neural Networks [J]. Computer Science, 2024, 51(9): 365-370.
[5] CHEN Liang, SUN Cong. Deep-learning Based DKOM Attack Detection for Linux System [J]. Computer Science, 2024, 51(9): 383-392.
[6] SUN Yumo, LI Xinhang, ZHAO Wenjie, ZHU Li, LIANG Ya’nan. Driving Towards Intelligent Future:The Application of Deep Learning in Rail Transit Innovation [J]. Computer Science, 2024, 51(8): 1-10.
[7] WANG Yiyang, LIU Fagui, PENG Lingxia, ZHONG Guoxiang. Out-of-Distribution Hard Disk Failure Prediction with Affinity Propagation Clustering and Broad Learning Systems [J]. Computer Science, 2024, 51(8): 63-74.
[8] WEI Xiangxiang, MENG Zhaohui. Hohai Graphic Protein Data Bank and Prediction Model [J]. Computer Science, 2024, 51(8): 117-123.
[9] GUO Fangyuan, JI Genlin. Video Anomaly Detection Method Based on Dual Discriminators and Pseudo Video Generation [J]. Computer Science, 2024, 51(8): 217-223.
[10] CHEN Shanshan, YAO Subin. Study on Recommendation Algorithms Based on Knowledge Graph and Neighbor PerceptionAttention Mechanism [J]. Computer Science, 2024, 51(8): 313-323.
[11] CHENG Xuefeng, DONG Minggang. Dynamic Multi-objective Optimization Algorithm Based on RNN Information Accumulation [J]. Computer Science, 2024, 51(8): 333-344.
[12] CAI Changjuan, ZHUANG Lei, YANG Sijin, WANG Jiaxing, YANG Xinyu. Variable-length Shaping Queue Adjustment Algorithm in Time-sensitive Networks [J]. Computer Science, 2024, 51(8): 354-363.
[13] ZENG Zihui, LI Chaoyang, LIAO Qing. Multivariate Time Series Anomaly Detection Algorithm in Missing Value Scenario [J]. Computer Science, 2024, 51(7): 108-115.
[14] HU Haibo, YANG Dan, NIE Tiezheng, KOU Yue. Graph Contrastive Learning Incorporating Multi-influence and Preference for Social Recommendation [J]. Computer Science, 2024, 51(7): 146-155.
[15] HAN Bing, DENG Lixiang, ZHENG Yi, REN Shuang. Survey of 3D Point Clouds Upsampling Methods [J]. Computer Science, 2024, 51(7): 167-196.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.