研究者基本情報

• 博士（情報科学）, 北海道大学, 2024年09月

• https://researchmap.jp/zhongrui1996

• https://orcid.org/0000-0003-4605-5579
• https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202401001374466048
• https://scholar.google.com/citations?user=xd1vrwIAAAAJ&hl=en

• https://orcid.org/0000-0003-4605-5579

• 202401001374466048

• ハイパーヒューリスティック
• メタヒューリスティック
• 最適化アルゴリズム
• 進化計算

• 情報通信, ソフトコンピューティング, メタヒューリスティック
• 情報通信, ソフトコンピューティング, 進化計算

研究活動情報

• 2024年09月, 北海道大学, 学院長賞

• Competitive cluster elimination (CCE) for large-scale swarm optimization
  Junbo Jacob Lian; Kaichen Ouyang; Yujun Zhang; Rui Zhong; Haoran Chen; Zikun Zheng; Yujun Sun; Huiling Chen
  Applied Soft Computing, 2026年08月
  研究論文（学術雑誌）
• Q-learning driven artificial lemming algorithm with elite pool and dynamic vertical crossover strategy: a case study in wind power forecasting
  Yaning Xiao; Yueqin Yin; Rui Zhong; Adam Slowik; Huiling Chen
  Expert Systems with Applications, 2026年07月
  研究論文（学術雑誌）
• IKUN: A mean-field game theoretic KD-tree density guided mechanism for evolutionary algorithms
  Junbo Jacob Lian; Mingyang Yu; Kaichen Ouyang; Shengwei Fu; Rui Zhong; Yujun Zhang; Huiling Chen
  Information Sciences, 2026年07月
  研究論文（学術雑誌）
• Multi-strategy quantum-enhanced RIME algorithm for underdetermined system of equations solution in grounding network corrosion diagnosis
  Jinhe Chen; Zhongmin Wang; Huiling Chen; Jun Yu; Rui Zhong
  Cluster Computing, 2026年06月
  研究論文（学術雑誌）
• A novel dynamic horned lizard algorithm with advanced strategies for high-dimensional optimization and pathology lung cancer image segmentation
  Mahmoud Abdel-Salam; Zahraa Tarek; Rui Zhong; Gang Hu; Nebojsa Bacanin
  Knowledge-Based Systems, 2026年06月
  研究論文（学術雑誌）
• Twisted convolutional networks (TCNs): Enhancing feature interactions for non-spatial data classification
  Junbo Jacob Lian; Haoran Chen; Kaichen Ouyang; Yujun Zhang; Rui Zhong; Huiling Chen
  Neural Networks, 2026年05月
  研究論文（学術雑誌）
• Parameter adaptive competitive differential evolution with local search
  Rui Zhong; Zhongmin Wang; Yaning Xiao; Yujun Zhang; Junbo Jacob Lian; Jun Yu; Zhennao Cai; Zhiyong Pan; Huiling Chen; Sudan Yu
  Applied Intelligence, 2026年04月
  研究論文（学術雑誌）
• Competitive differential evolution with success-failure adaptation mechanism: performance benchmarking and application in eggplant disease diagnosis
  Zhongmin Wang; Daihong Li; Jun Yu; Mahmoud Abdel-Salam; Gang Hu; Essam H. Houssein; Nagwan Abdel Samee; Rui Zhong
  International Journal of Machine Learning and Cybernetics, 2026年04月
  研究論文（学術雑誌）
• K-means competitive swarm optimizer: performance benchmarking and application in brain tumor detection
  Yang Cao; Zhongmin Wang; Xingbang Du; Jun Yu; Rui Zhong; Masaharu Munetomo
  International Journal of Machine Learning and Cybernetics, 2026年03月
  研究論文（学術雑誌）
• Adaptive success history adaptive differential evolution with multi-agent participated competition mechanism
  Zhongmin Wang; Qifang Luo; Chuan Zhang; Shiwei Chen; Jun Yu; Mahmoud Abdel-Salam; Rui Zhong; Daihong Li
  Cluster Computing, 2026年02月
  研究論文（学術雑誌）
• SESCA: An Enhanced Sine-Cosine Algorithm with Sigmoid-Based Nonlinear Mapping for Optimization
  Yang Cao; Xingbang Du; Rui Zhong; Jun Yu; Masaharu Munetomo
  2026年
• Forecasting Renewable Energy and Electricity Consumption Using Evolutionary Computation
  Yang Cao; Rui Zhong; Jun Yu
  2026年
• Enhancing Competitive Swarm Optimization Through Time-Adaptive Selection Between Adjacency-Guided and Random Strategies
  Yang Cao; Rui Zhong; Jun Yu; Masaharu Munetomo
  2026年
• RETRACTED: Liu et al. Next Point of Interest (POI) Recommendation System Driven by User Probabilistic Preferences and Temporal Regularities. Mathematics 2025, 13, 1232
  Fengyu Liu; Jinhe Chen; Jun Yu; Rui Zhong
  Mathematics, 2025年12月29日
  研究論文（学術雑誌）
• Advanced design for nonlinear photovoltaic system problems: A co-evolutionary framework based on a decomposition approach
  Yujun Zhang; Wenyin Gong; Rui Zhong; Huiling Chen; Jun Yu; Junbo Jacob Lian; Juan Zhao; Zhengming Gao
  Swarm and Evolutionary Computation, 2025年12月
  研究論文（学術雑誌）
• Tri-subpopulation sigmoid-enhanced sine-cosine algorithm and its application to gene function prediction problem
  Yang Cao; Yuefeng Xu; Xingbang Du; Rui Zhong; Jun Yu; Masaharu Munetomo
  JOURNAL OF SUPERCOMPUTING, 81, 17, 2025年11月19日
  英語, 研究論文（学術雑誌）
• Dynamic fitness-distance balance competitive swarm optimizer: performance investigation, engineering Simulation, and application in superconductor critical temperature prediction
  Yang Cao; Xingbang Du; Jun Yu; Rui Zhong; Masaharu Munetomo
  Cluster Computing, 2025年10月
  研究論文（学術雑誌）
• Trend-Aware Mechanism for Metaheuristic Algorithms
  Junbo Jacob Lian; Kaichen Ouyang; Rui Zhong; Yujun Zhang; Shipeng Luo; Ling Ma; Xincan Wu; Huiling Chen
  Applied Soft Computing, 2025年10月, ［査読有り］
  研究論文（学術雑誌）
• Multi-strategies improved coati optimization algorithm and performance analysis
  Chunqing Li; Zhongmin Wang; Jun Yu; Mahmoud Abdel-Salam; Essam H. Houssein; Rui Zhong
  Knowledge and Information Systems, Springer Science and Business Media LLC, 2025年08月22日, ［査読有り］, ［最終著者, 責任著者］
  研究論文（学術雑誌）
• IECO: an improved educational competition optimizer for state-of-the-art engineering optimization
  Xiaojie Tang; Junbo Jacob Lian; Ling Ma; Xincan Wu; Rui Zhong; Yujun Zhang; Huiling Chen
  Journal of Big Data, 12, 1, Springer Science and Business Media LLC, 2025年08月19日, ［査読有り］
  研究論文（学術雑誌）
• Self-adaptive competitive swarm optimizer: a memetic approach for global optimization and human-powered aircraft design
  Rui Zhong; Zhongmin Wang; Ibrahim Al-Shourbaji; Essam H. Houssein; Pramod H. Kachare; Abdoh Jabbari; Raimund Kirner; Jun Yu
  Memetic Computing, 17, 3, Springer Science and Business Media LLC, 2025年06月23日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Leveraging Inter-Generational Knowledge Transfer in Large-Scale Global Optimization
  Yuefeng Xu; Rui Zhong; Chong Zhou; Chao Zhang; Jun Yu
  2025 IEEE Congress on Evolutionary Computation (CEC), 1, 4, IEEE, 2025年06月08日, ［査読有り］
  研究論文（国際会議プロシーディングス）
• Improved Competitive Swarm Optimizer with Linear Population Reduction for Large-scale Optimization
  Rui Zhong; Jun Yu; Xingbang Du; Enzhi Zhang; Abdelazim G. Hussien
  2025 IEEE Congress on Evolutionary Computation (CEC), 1, 8, IEEE, 2025年06月08日, ［査読有り］, ［筆頭著者］
  研究論文（国際会議プロシーディングス）
• Uniaxial compressive strength of concrete inversion using machine learning and computational intelligence approach
  Yuefeng Li; Rui Zhong; Jun Yu; Jiefang Song; Qiwei Wang; Chengzhi Chen; Xiangyang Li; Enlong Liu
  Results in Engineering, 26, 105627, 105627, Elsevier BV, 2025年06月, ［査読有り］
  研究論文（学術雑誌）
• Enhanced Crested Ibis Algorithm: Performance Validation in Benchmark Functions, Engineering Problem, and Application in Brain Tumor Detection
  Rui Zhong; Abdelazim G. Hussien; Essam H. Houssein; Jun Yu
  Expert Systems with Applications, 128231, 128231, Elsevier BV, 2025年05月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• SFE-EANDS: a simple, fast, and efficient algorithm with external archive and normalized distance-based selection for high-dimensional feature selection
  Rui Zhong; Yang Cao; Essam H. Houssein; Jun Yu; Masaharu Munetomo
  Cluster Computing, 28, 5, Springer Science and Business Media LLC, 2025年04月28日, ［査読有り］, ［筆頭著者, 責任著者］
  研究論文（学術雑誌）
• Adjacent Distance Matrix-Based Competitive Swarm Optimizer
  Yang Cao; Rui Zhong; Jun Yu; Masaharu Munetomo
  Lecture Notes in Computer Science, 38, 51, Springer Nature Switzerland, 2025年04月17日, ［査読有り］, ［責任著者］
  論文集(書籍)内論文
• Competitive differential evolution with knowledge inheritance for single-objective human-powered aircraft design
  Rui Zhong; Yang Cao; Enzhi Zhang; Masaharu Munetomo
  The Journal of Supercomputing, 81, 5, Springer Science and Business Media LLC, 2025年04月10日, ［査読有り］, ［筆頭著者, 責任著者］
  研究論文（学術雑誌）
• HHDE: a hyper-heuristic differential evolution with novel boundary repair technique for complex optimization
  Rui Zhong; Shilong Zhang; Jun Yu; Masaharu Munetomo
  The Journal of Supercomputing, 81, 5, Springer Science and Business Media LLC, 2025年04月04日, ［査読有り］, ［筆頭著者, 責任著者］
  研究論文（学術雑誌）
• Vision Transformer-Based Meta Loss Landscape Exploration with Actor-Critic Method
  Enzhi Zhang; Rui Zhong; Xingbang Du; Mohamed Wahib; Masaharu Munetomo
  Communications in Computer and Information Science, 291, 305, Springer Nature Switzerland, 2025年03月26日, ［査読有り］
  論文集(書籍)内論文
• Hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization
  Rui Zhong; Jun Yu; Masaharu Munetomo
  Communications in Computer and Information Science, 264, 277, Springer Nature Switzerland, 2025年03月26日, ［査読有り］, ［筆頭著者, 責任著者］
  論文集(書籍)内論文
• Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization
  Rui Zhong; Yang Cao; Enzhi Zhang; Masaharu Munetomo
  Communications in Computer and Information Science, 251, 263, Springer Nature Switzerland, 2025年03月26日, ［査読有り］, ［筆頭著者, 責任著者］
  論文集(書籍)内論文
• A Comprehensive Review of the Tunicate Swarm Algorithm: Variations, Applications, and Results
  Rong Zheng; Abdelazim G. Hussien; Anas Bouaouda; Rui Zhong; Gang Hu
  Archives of Computational Methods in Engineering, Springer Science and Business Media LLC, 2025年03月12日, ［査読有り］
  研究論文（学術雑誌）
• Under Complex Wind Scenarios: Considering Large-scale Wind Turbines in Wind Farm Layout Optimization via Self-adaptive Optimal Fractional-order Guided Differential Evolution
  Yujun Zhang; Zihang Zhang; Rui Zhong; Jun Yu; Essam H. Houssein; Juan Zhao; Zhengming Gao
  Energy, 135866, 135866, Elsevier BV, 2025年03月, ［査読有り］
  研究論文（学術雑誌）
• Integrating Competitive Framework into Differential Evolution: Comprehensive performance analysis and application in brain tumor detection
  Rui Zhong; Zhongmin Wang; Yujun Zhang; Junbo Jacob Lian; Jun Yu; Huiling Chen
  Applied Soft Computing, 112995, 112995, Elsevier BV, 2025年03月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Space mission trajectory optimization via competitive differential evolution with independent success history adaptation
  Rui Zhong; Abdelazim G. Hussien; Shilong Zhang; Yuefeng Xu; Jun Yu
  Applied Soft Computing, 2025年03月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• LLMOA: A novel large language model assisted hyper-heuristic optimization algorithm
  Rui Zhong; Abdelazim G. Hussien; Jun Yu; Masaharu Munetomo
  Advanced Engineering Informatics, 64, 103042, 103042, Elsevier BV, 2025年03月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction: Performance benchmarking and application in eye disease detection
  Rui Zhong; Zhongmin Wang; Abdelazim G. Hussien; Essam H. Houssein; Ibrahim Al-Shourbaji; Mohamed A. Elseify; Jun Yu
  Computers in Biology and Medicine, 186, 109587, 109587, Elsevier BV, 2025年03月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Multi-strategy enhanced marine predator algorithm: performance investigation and application in intrusion detection
  Zhongmin Wang; Yujun Zhang; Jun Yu; YuanYuan Gao; Guangwei Zhao; Essam H. Houssein; Rui Zhong
  Journal of Big Data, 12, 1, Springer Science and Business Media LLC, 2025年02月19日, ［査読有り］, ［最終著者, 責任著者］
  研究論文（学術雑誌）
• Crested ibis algorithm and its application in human-powered aircraft design
  Yuefeng Xu; Rui Zhong; Chao Zhang; Jun Yu
  Knowledge-Based Systems, 2025年02月, ［査読有り］
  研究論文（学術雑誌）
• Efficient multiplayer battle game optimizer for numerical optimization and adversarial robust neural architecture search
  Rui Zhong; Yuefeng Xu; Chao Zhang; Jun Yu
  Alexandria Engineering Journal, 113, 150, 168, Elsevier BV, 2025年02月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Group-driven Remora Optimization Algorithm with multiple search and regeneration strategies
  Fei Peng; Rui Zhong; Chao Zhang; Jun Yu
  Cluster Computing, 28, 3, Springer Science and Business Media LLC, 2025年01月21日
  研究論文（学術雑誌）
• Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm
  Yang Cao; Jun Yu; Rui Zhong; Masaharu Munetomo
  Scientific Reports, 15, 1, Springer Science and Business Media LLC, 2025年01月20日, ［査読有り］, ［責任著者］
  研究論文（学術雑誌）
• Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training
  Rui Zhong; Chao Zhang; Jun Yu
  Alexandria Engineering Journal, 110, 77, 98, Elsevier BV, 2025年01月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Vision transformer-based meta loss landscape exploration with actor-critic method
  Enzhi Zhang; Rui Zhong; Xingbang Du; Mohamed Wahib; Masaharu Munetomo
  The Journal of Supercomputing, 81, 1, Springer Science and Business Media LLC, 2024年12月28日, ［査読有り］
  研究論文（学術雑誌）
• Symbiotic mechanism-based honey badger algorithm for continuous optimization
  Yuefeng Xu; Rui Zhong; Yang Cao; Chao Zhang; Jun Yu
  Cluster Computing, 28, 2, Springer Science and Business Media LLC, 2024年11月26日, ［査読有り］
  研究論文（学術雑誌）
• Simplified Multiplayer Battle Game-inspired Optimizer with Diverse Search Strategies
  Shilong ZHANG; Yuefeng XU; Rui ZHONG; Chao ZHANG; Jun YU
  2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 1, 6, IEEE, 2024年11月09日, ［査読有り］
  研究論文（国際会議プロシーディングス）
• Accelerating Vegetation Evolution with Gradient Descent and Martingale Strategies
  Fei Peng; Rui Zhong; Chao Zhang; Jun Yu
  2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT), 235, 240, IEEE, 2024年11月08日
  研究論文（国際会議プロシーディングス）
• Improved snow ablation optimization for multilevel threshold image segmentation
  Rui Zhong; Chao Zhang; Jun Yu
  Cluster Computing, 28, 1, Springer Science and Business Media LLC, 2024年10月18日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• An efficient improved parrot optimizer for bladder cancer classification
  Essam H. Houssein; Marwa M. Emam; Waleed Alomoush; Nagwan Abdel Samee; Mona M. Jamjoom; Rui Zhong; Krishna Gopal Dhal
  Computers in Biology and Medicine, 181, 109080, 109080, Elsevier BV, 2024年10月, ［査読有り］
  研究論文（学術雑誌）
• Evolutionary Computation with Distance-Based Pretreatment for Multi-modal Problems
  Yuefeng Xu; Rui Zhong; Chao Zhang; Jun Yu
  Lecture Notes in Computer Science, 313, 322, Springer Nature Singapore, 2024年08月21日, ［査読有り］
  論文集(書籍)内論文
• Hierarchical Adaptive Differential Evolution with Local Search for Extreme Learning Machine
  Rui Zhong; Yang Cao; Jun Yu; Masaharu Munetomo
  Lecture Notes in Computer Science, 235, 246, Springer Nature Singapore, 2024年08月21日, ［査読有り］, ［筆頭著者］
  論文集(書籍)内論文
• Large Language Model Assisted Adversarial Robustness Neural Architecture Search
  Rui Zhong; Yang Cao; Jun Yu; Masaharu Munetomo
  2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 433, 437, IEEE, 2024年08月16日, ［査読有り］, ［筆頭著者］
  研究論文（国際会議プロシーディングス）
• Optimization of Electricity Consumption Forecasting Models via Hyper-Heuristic Algorithm
  Yang Cao; Rui Zhong; Jun Yu; Masaharu Munetomo
  2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 114, 120, IEEE, 2024年08月16日, ［査読有り］
  研究論文（国際会議プロシーディングス）
• GeminiDE: A Novel Parameter Adaptation Scheme in Differential Evolution
  Rui Zhong; Shilong Zhang; Jun Yu; Masaharu Munetomo
  2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 33, 38, IEEE, 2024年08月16日, ［査読有り］, ［筆頭著者］
  研究論文（国際会議プロシーディングス）
• Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems
  Rui Zhong; Chao Zhang; Jun Yu
  Knowledge and Information Systems, 66, 11, 6933, 6974, Springer Science and Business Media LLC, 2024年07月15日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Leveraging large language model to generate a novel metaheuristic algorithm with CRISPE framework
  Rui Zhong; Yuefeng Xu; Chao Zhang; Jun Yu
  Cluster Computing, 27, 10, 13835, 13869, Springer Science and Business Media LLC, 2024年07月06日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Validation Loss Landscape Exploration with Deep Q-Learning
  Enzhi Zhang; Rui Zhong; Masaharu Munetomo; Mohamed Wahib
  2024 International Joint Conference on Neural Networks (IJCNN), 1, 9, IEEE, 2024年06月30日, ［査読有り］
  研究論文（国際会議プロシーディングス）
• Gene-targeting multiplayer battle game optimizer for large-scale global optimization via cooperative coevolution
  Rui Zhong; Jun Yu
  Cluster Computing, 27, 9, 12483, 12508, Springer Science and Business Media LLC, 2024年06月14日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• DEA$$^2$$H$$^2$$: differential evolution architecture based adaptive hyper-heuristic algorithm for continuous optimization
  Rui Zhong; Jun Yu
  Cluster Computing, 27, 9, 12239, 12266, Springer Science and Business Media LLC, 2024年06月08日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• A novel evolutionary status guided hyper-heuristic algorithm for continuous optimization
  Rui Zhong; Jun Yu
  Cluster Computing, 27, 9, 12209, 12238, Springer Science and Business Media LLC, 2024年06月08日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Cooperative coevolutionary differential evolution with adjacent intensity matrix with linkage identification for large-scale optimization problems in noisy environments
  Rui Zhong; Binnan Tu; Enzhi Zhang; Masaharu Munetomo
  Evolutionary Intelligence, 17, 5-6, 3483, 3503, Springer Science and Business Media LLC, 2024年05月10日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Hybrid remora crayfish optimization for engineering and wireless sensor network coverage optimization
  Rui Zhong; Qinqin Fan; Chao Zhang; Jun Yu
  Cluster Computing, 27, 7, 10141, 10168, Springer Science and Business Media LLC, 2024年05月04日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Multiplayer battle game-inspired optimizer for complex optimization problems
  Yuefeng Xu; Rui Zhong; Chao Zhang; Jun Yu
  Cluster Computing, 27, 6, 8307, 8331, Springer Science and Business Media LLC, 2024年04月10日, ［査読有り］
  研究論文（学術雑誌）
• Meta generative image and text data augmentation optimization
  Enzhi Zhang; Bochen Dong; Mohamed Wahib; Rui Zhong; Masaharu Munetomo
  The Journal of Supercomputing, 80, 9, 12644, 12662, Springer Science and Business Media LLC, 2024年02月19日, ［査読有り］
  研究論文（学術雑誌）
• SRIME: a strengthened RIME with Latin hypercube sampling and embedded distance-based selection for engineering optimization problems
  Rui Zhong; Jun Yu; Chao Zhang; Masaharu Munetomo
  Neural Computing and Applications, 36, 12, 6721, 6740, Springer Science and Business Media LLC, 2024年02月12日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Evolutionary multi-mode slime mold optimization: a hyper-heuristic algorithm inspired by slime mold foraging behaviors
  Rui Zhong; Enzhi Zhang; Masaharu Munetomo
  The Journal of Supercomputing, 80, 9, 12186, 12217, Springer Science and Business Media LLC, 2024年02月09日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Learning from the Past Training Trajectories: Regularization by Validation
  Enzhi Zhang; Mohamed Wahib; Rui Zhong; Masaharu Munetomo
  Journal of Advanced Computational Intelligence and Intelligent Informatics, 28, 1, 67, 78, Fuji Technology Press Ltd., 2024年01月20日, ［査読有り］
  研究論文（学術雑誌）, Deep model optimization methods discard the training weights which contain information about the validation loss landscape that can guide further model optimization. In this paper, we first show that a supervisor neural network can be used to predict the validation losses or accuracy of another deep model (student) through its discarded training weights. Then based on this behavior, we propose a weight-loss (accuracy) pair-based training framework called regularization by validation to help decrease overfitting and increase the generalization performance of the student model by predicting the validation losses. We conduct our experiments on the MNIST, CIFAR-10, and CIFAR-100 datasets with the multilayer perceptron and ResNet-56 to show that we can improve the generalization performance with the past training trajectories.
• Chaotic vegetation evolution: leveraging multiple seeding strategies and a mutation module for global optimization problems
  Rui Zhong; Chao Zhang; Jun Yu
  Evolutionary Intelligence, 17, 4, 2387, 2411, Springer Science and Business Media LLC, 2024年01月14日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Q-learning based vegetation evolution for numerical optimization and wireless sensor network coverage optimization
  Rui Zhong; Fei Peng; Jun Yu; Masaharu Munetomo
  Alexandria Engineering Journal, 87, 148, 163, Elsevier BV, 2024年01月, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）
• Optimal Defense Resource Allocation Considering Nonlinear Attack Cost in Power Systems
  Bowen Xu; Mengxiang Liu; Rui Zhong; Ke Zuo; Ruilong Deng
  IEEE Transactions on Industrial Informatics, 1, 10, Institute of Electrical and Electronics Engineers (IEEE), 2024年, ［査読有り］
  研究論文（学術雑誌）
• Improving Sailfish Optimizer with Population Switching Strategy and Random Mutation Strategy
  Fei Peng; Rui Zhong; Qinqin Fan; Chao Zhang; Jun Yu
  2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT), 713, 720, IEEE, 2023年11月10日, ［査読有り］
  研究論文（国際会議プロシーディングス）
• Surrogate Ensemble-Assisted Hyper-Heuristic Algorithm for Expensive Optimization Problems
  Rui Zhong; Jun Yu; Chao Zhang; Masaharu Munetomo
  International Journal of Computational Intelligence Systems, 16, 1, Springer Science and Business Media LLC, 2023年10月26日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）, Abstract
  
  This paper proposes a novel surrogate ensemble-assisted hyper-heuristic algorithm (SEA-HHA) to solve expensive optimization problems (EOPs). A representative HHA consists of two parts: the low-level and the high-level components. In the low-level component, we regard the surrogate-assisted technique as a type of search strategy and design the four search strategy archives: exploration strategy archive, exploitation strategy archive, surrogate-assisted estimation archive, and mutation strategy archive as low-level heuristics (LLHs), each archive contains one or more search strategies. Once the surrogate-assisted estimation archive is activated to generate the offspring individual, SEA-HHA first selects the dataset for model construction from three principles: All Data, Recent Data, and Neighbor, which correspond to the global and the local surrogate model, respectively. Then, the dataset is randomly divided into training and validation data, and the most accurate model built by polynomial regression (PR), support vector regression (SVR), and Gaussian process regression (GPR) cooperates with the infill sampling criterion is employed for solution estimation. In the high-level component, we design a random selection function based on the pre-defined probabilities to manipulate a set of LLHs. In numerical experiments, we compare SEA-HHA with six optimization techniques on 5-D, 10-D, and 30-D CEC2013 benchmark functions and three engineering optimization problems with only 1000 fitness evaluation times (FEs). The experimental and statistical results show that our proposed SEA-HHA has broad prospects for dealing with EOPs.
• Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems
  Rui Zhong; Enzhi Zhang; Masaharu Munetomo
  Complex & Intelligent Systems, 10, 2, 2129, 2149, Springer Science and Business Media LLC, 2023年10月26日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）, Abstract
  
  This paper proposes a novel algorithm named surrogate ensemble assisted differential evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale expensive optimization problems (LSEOPs) based on the CC framework. In the decomposition phase, our proposed EDDG inherits the framework of efficient recursive differential grouping (ERDG) and embeds the multiplicative interaction identification technique of Dual DG (DDG), which can detect the additive and multiplicative interactions simultaneously without extra fitness evaluation consumption. Inspired by RDG2 and RDG3, we design the adaptive determination threshold and further decompose relatively large-scale sub-components to alleviate the curse of dimensionality. In the optimization phase, the SEADE is adopted as the basic optimizer, where the global and the local surrogate model are constructed by generalized regression neural network (GRNN) with all historical samples and Gaussian process regression (GPR) with recent samples. Expected improvement (EI) infill sampling criterion cooperated with random search is employed to search elite solutions in the surrogate model. To evaluate the performance of our proposal, we implement comprehensive experiments on CEC2013 benchmark functions compared with state-of-the-art decomposition techniques. Experimental and statistical results show that our proposed EDDG is competitive with these advanced decomposition techniques, and the introduction of SEADE can accelerate the convergence of optimization significantly.
• Training Knowledge Inheritance Through Deep Q-Net
  Enzhi Zhang; Ruqin Wang; Mohamed Wahib; Rui Zhong; Masaharu Munetomo
  2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 899, 904, IEEE, 2023年10月01日, ［査読有り］
  研究論文（国際会議プロシーディングス）
• Vegetation Evolution with Dynamic Maturity Strategy and Diverse Mutation Strategy for Solving Optimization Problems
  Rui Zhong; Fei Peng; Enzhi Zhang; Jun Yu; Masaharu Munetomo
  Biomimetics, 8, 6, 454, 454, MDPI AG, 2023年09月25日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）, We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a higher probability of generating more seed individuals. Here, all individuals will first become allocated to generate a fixed number of seeds, and then the remaining number of allocatable seeds will be distributed competitively according to their fitness. Since VEGE performs poorly in getting rid of local optima, we propose the diverse mutation strategy as the second search operator with several different mutation methods to increase the diversity of seed individuals. In other words, each generated seed individual will randomly choose one of the methods to mutate with a lower probability. To evaluate the performances of the two proposed strategies, we run our proposal (VEGE + two strategies), VEGE, and another seven advanced evolutionary algorithms (EAs) on the CEC2013 benchmark functions and seven popular engineering problems. Finally, we analyze the respective contributions of these two strategies to VEGE. The experimental and statistical results confirmed that our proposal can significantly accelerate convergence and improve the convergence accuracy of the conventional VEGE in most optimization problems.
• Evolutionary Multi-Mode Slime Mould Optimization: A Hyper-Heuristic Algorithm Inspired by Slime Mould Foraging Behaviors
  Rui Zhong; Enzhi Zhang; Masaharu Munetomo
  2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), 2153, 2160, IEEE, 2023年07月24日, ［査読有り］, ［筆頭著者］
  研究論文（国際会議プロシーディングス）
• Meta Generative Data Augmentation Optimization
  Enzhi Zhang; Bochen Dong; Mohamed Wahib; Rui Zhong; Masaharu Munetomo
  2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), 2218, 2225, IEEE, 2023年07月24日, ［査読有り］
  研究論文（国際会議プロシーディングス）
• Adjacent Intensity Matrix with Linkage Identification for Large-Scale Optimization in Noisy Environments
  Rui Zhong; Binan Tu; Enzhi Zhang; Masaharu Munetomo
  2023 IEEE Congress on Evolutionary Computation (CEC), 01, 10, IEEE, 2023年07月01日, ［査読有り］, ［筆頭著者］
  研究論文（国際会議プロシーディングス）
• A Hierarchical Cooperative Coevolutionary Approach to Solve Very Large-Scale Traveling Salesman Problem
  Rui Zhong; Enzhi Zhang; Masaharu Munetomo
  Communications in Computer and Information Science, 74, 84, Springer Nature Switzerland, 2023年05月27日, ［査読有り］, ［筆頭著者］
  論文集(書籍)内論文
• Cooperative Coevolutionary NSGA-II with Linkage Measurement Minimization for Large-Scale Multi-objective Optimization
  Rui Zhong; Masaharu Munetomo
  Lecture Notes in Computer Science, 43, 55, Springer Nature Switzerland, 2023年03月09日, ［査読有り］, ［筆頭著者］
  論文集(書籍)内論文
• Cooperative coevolutionary differential evolution with linkage measurement minimization for large-scale optimization problems in noisy environments
  Rui Zhong; Enzhi Zhang; Masaharu Munetomo
  Complex & Intelligent Systems, 9, 4, 4439, 4456, Springer Science and Business Media LLC, 2023年01月17日, ［査読有り］, ［筆頭著者］
  研究論文（学術雑誌）, Abstract
  
  Many optimization problems suffer from noise, and the noise combined with the large-scale attributes makes the problem complexity explode. Cooperative coevolution (CC) based on divide and conquer decomposes the problems and solves the sub-problems alternately, which is a popular framework for solving large-scale optimization problems (LSOPs). Many studies show that the CC framework is sensitive to decomposition, and the high-accuracy decomposition methods such as differential grouping (DG), DG2, and recursive DG (RDG) are extremely sensitive to sampling accuracy, which will fail to detect the interactions in noisy environments. Therefore, solving LSOPs in noisy environments based on the CC framework faces unprecedented challenges. In this paper, we propose a novel decomposition method named linkage measurement minimization (LMM). We regard the decomposition problem as a combinatorial optimization problem and design the linkage measurement function (LMF) based on Linkage Identification by non-linearity check for real-coded GA (LINC-R). A detailed theoretical analysis explains why our proposal can determine the interactions in noisy environments. In the optimization, we introduce an advanced optimizer named modified differential evolution with distance-based selection (MDE-DS), and the various mutation strategy and distance-based selection endow MDE-DS with strong anti-noise ability. Numerical experiments show that our proposal is competitive with the state-of-the-art decomposition methods in noisy environments, and the introduction of MDE-DS can accelerate the optimization in noisy environments significantly.
• Prediction of Bone Metastasis in Breast Cancer Based on Minimal Driver Gene Set in Gene Dependency Network
  Jia-Nuo Li; Rui Zhong; Xiong-Hui Zhou
  Genes, 10, 6, 466, 466, MDPI AG, 2019年06月17日, ［査読有り］
  研究論文（学術雑誌）, Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene and the risk of bone metastasis might be affected by another gene. Then, based on the structure controllability theory, we mined the driver gene set which can control the whole network in the gene dependency network, and the signature genes were selected from them. Survival analysis showed that the signature could distinguish the bone metastasis risks of cancer patients in the test data set and independent data set. Besides, we used the signature genes to construct a centroid classifier. The results showed that our method is effective and performed better than published methods.

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