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ZHONG Rui
| Information Initiative Center Systems Design | Specially Appointed Assistant Professor |
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- 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, Aug. 2026
Scientific journal - 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, Jul. 2026
Scientific journal - 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, Jul. 2026
Scientific journal - 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, Jun. 2026
Scientific journal - 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, Jun. 2026
Scientific journal - 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, May 2026
Scientific journal - 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, Apr. 2026
Scientific journal - 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, Apr. 2026
Scientific journal - 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, Mar. 2026
Scientific journal - 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, Feb. 2026
Scientific journal - SESCA: An Enhanced Sine-Cosine Algorithm with Sigmoid-Based Nonlinear Mapping for Optimization
Yang Cao; Xingbang Du; Rui Zhong; Jun Yu; Masaharu Munetomo
2026 - Forecasting Renewable Energy and Electricity Consumption Using Evolutionary Computation
Yang Cao; Rui Zhong; Jun Yu
2026 - Enhancing Competitive Swarm Optimization Through Time-Adaptive Selection Between Adjacency-Guided and Random Strategies
Yang Cao; Rui Zhong; Jun Yu; Masaharu Munetomo
2026 - 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, 29 Dec. 2025
Scientific journal - 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, Dec. 2025
Scientific journal - 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, 19 Nov. 2025
English, Scientific journal - 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, Oct. 2025
Scientific journal - 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, Oct. 2025, [Peer-reviewed]
Scientific journal - 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, 22 Aug. 2025, [Peer-reviewed], [Last author, Corresponding author]
Scientific journal - 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, 19 Aug. 2025, [Peer-reviewed]
Scientific journal - 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, 23 Jun. 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, 08 Jun. 2025, [Peer-reviewed]
International conference proceedings - 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, 08 Jun. 2025, [Peer-reviewed], [Lead author]
International conference proceedings - 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, Jun. 2025, [Peer-reviewed]
Scientific journal - 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, May 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, 28 Apr. 2025, [Peer-reviewed], [Lead author, Corresponding author]
Scientific journal - 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, 17 Apr. 2025, [Peer-reviewed], [Corresponding author]
In book - 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, 10 Apr. 2025, [Peer-reviewed], [Lead author, Corresponding author]
Scientific journal - 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, 04 Apr. 2025, [Peer-reviewed], [Lead author, Corresponding author]
Scientific journal - 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, 26 Mar. 2025, [Peer-reviewed]
In book - 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, 26 Mar. 2025, [Peer-reviewed], [Lead author, Corresponding author]
In book - 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, 26 Mar. 2025, [Peer-reviewed], [Lead author, Corresponding author]
In book - 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, 12 Mar. 2025, [Peer-reviewed]
Scientific journal - 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, Mar. 2025, [Peer-reviewed]
Scientific journal - 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, Mar. 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, Mar. 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, Mar. 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, Mar. 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, 19 Feb. 2025, [Peer-reviewed], [Last author, Corresponding author]
Scientific journal - Crested ibis algorithm and its application in human-powered aircraft design
Yuefeng Xu; Rui Zhong; Chao Zhang; Jun Yu
Knowledge-Based Systems, Feb. 2025, [Peer-reviewed]
Scientific journal - 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, Feb. 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, 21 Jan. 2025
Scientific journal - 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, 20 Jan. 2025, [Peer-reviewed], [Corresponding author]
Scientific journal - 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, Jan. 2025, [Peer-reviewed], [Lead author]
Scientific journal - 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, 28 Dec. 2024, [Peer-reviewed]
Scientific journal - 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, 26 Nov. 2024, [Peer-reviewed]
Scientific journal - 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, 09 Nov. 2024, [Peer-reviewed]
International conference proceedings - 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, 08 Nov. 2024
International conference proceedings - 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, 18 Oct. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, Oct. 2024, [Peer-reviewed]
Scientific journal - 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, 21 Aug. 2024, [Peer-reviewed]
In book - 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, 21 Aug. 2024, [Peer-reviewed], [Lead author]
In book - 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, 16 Aug. 2024, [Peer-reviewed], [Lead author]
International conference proceedings - 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, 16 Aug. 2024, [Peer-reviewed]
International conference proceedings - 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, 16 Aug. 2024, [Peer-reviewed], [Lead author]
International conference proceedings - 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, 15 Jul. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 06 Jul. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 30 Jun. 2024, [Peer-reviewed]
International conference proceedings - 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, 14 Jun. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 08 Jun. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 08 Jun. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 10 May 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 04 May 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 10 Apr. 2024, [Peer-reviewed]
Scientific journal - 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, 19 Feb. 2024, [Peer-reviewed]
Scientific journal - 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, 12 Feb. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, 09 Feb. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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., 20 Jan. 2024, [Peer-reviewed]
Scientific journal, 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, 14 Jan. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, Jan. 2024, [Peer-reviewed], [Lead author]
Scientific journal - 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, [Peer-reviewed]
Scientific journal - 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, 10 Nov. 2023, [Peer-reviewed]
International conference proceedings - 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, 26 Oct. 2023, [Peer-reviewed], [Lead author]
Scientific journal, 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, 26 Oct. 2023, [Peer-reviewed], [Lead author]
Scientific journal, 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, 01 Oct. 2023, [Peer-reviewed]
International conference proceedings - 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, 25 Sep. 2023, [Peer-reviewed], [Lead author]
Scientific journal, 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, 24 Jul. 2023, [Peer-reviewed], [Lead author]
International conference proceedings - 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, 24 Jul. 2023, [Peer-reviewed]
International conference proceedings - 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, 01 Jul. 2023, [Peer-reviewed], [Lead author]
International conference proceedings - 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, 27 May 2023, [Peer-reviewed], [Lead author]
In book - 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, 09 Mar. 2023, [Peer-reviewed], [Lead author]
In book - 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, 17 Jan. 2023, [Peer-reviewed], [Lead author]
Scientific journal, 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, 17 Jun. 2019, [Peer-reviewed]
Scientific journal, 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.
