Jun Zhang mostly deals with Mathematical optimization, Evolutionary algorithm, Particle swarm optimization, Algorithm design and Artificial intelligence. His work investigates the relationship between Mathematical optimization and topics such as Benchmark that intersect with problems in Global optimization. His Evolutionary algorithm research is multidisciplinary, incorporating perspectives in Evolutionary computation, Schedule, Differential evolution and Wireless sensor network.
The study incorporates disciplines such as Computational complexity theory and Heuristic in addition to Particle swarm optimization. His Algorithm design study incorporates themes from Time complexity and Vehicle routing problem. His Artificial intelligence study combines topics in areas such as Genetic algorithm and Machine learning.
Mathematical optimization, Artificial intelligence, Optimization problem, Evolutionary algorithm and Particle swarm optimization are his primary areas of study. His research investigates the link between Mathematical optimization and topics such as Benchmark that cross with problems in Convergence. His Artificial intelligence research is multidisciplinary, relying on both Algorithm design, Machine learning, Computer vision and Pattern recognition.
His Optimization problem study frequently draws connections between adjacent fields such as Multi-objective optimization. His research integrates issues of Swarm behaviour and Metaheuristic in his study of Multi-swarm optimization. His research links Algorithm with Evolutionary computation.
His primary areas of study are Mathematical optimization, Optimization problem, Evolutionary algorithm, Particle swarm optimization and Evolutionary computation. His Mathematical optimization study frequently links to other fields, such as Process. His research in Optimization problem intersects with topics in Convergence, Differential evolution, Multi-objective optimization and Benchmark.
His Evolutionary algorithm study is concerned with the field of Artificial intelligence as a whole. His Particle swarm optimization study often links to related topics such as Distributed computing. The Evolutionary computation study combines topics in areas such as Theoretical computer science and Computational intelligence.
Jun Zhang mainly focuses on Optimization problem, Mathematical optimization, Evolutionary algorithm, Benchmark and Particle swarm optimization. The concepts of his Optimization problem study are interwoven with issues in Test data generation, Differential evolution, Artificial intelligence and Shortest path problem. His Multi-objective optimization study in the realm of Mathematical optimization connects with subjects such as Supply chain network.
His study in Evolutionary algorithm is interdisciplinary in nature, drawing from both Evolutionary computation, Data-driven, Linear programming and Heuristic. His Particle swarm optimization research includes elements of Local optimum, Virtualization, Distributed computing, Metaheuristic and Heuristic. His Algorithm research focuses on Convergence and how it relates to Local search.
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Adaptive Particle Swarm Optimization
Zhi-Hui Zhan;Jun Zhang;Yun Li;H.S.-H. Chung.
systems man and cybernetics (2009)
Phase Diagram and High Temperature Superconductivity at 65 K in Tuning Carrier Concentration of Single-Layer FeSe Films
Shaolong He;Junfeng He;Wenhao Zhang;Lin Zhao.
arXiv: Superconductivity (2012)
Orthogonal Learning Particle Swarm Optimization
Zhi-Hui Zhan;Jun Zhang;Yun Li;Yu-Hui Shi.
IEEE Transactions on Evolutionary Computation (2011)
Phase diagram and electronic indication of high-temperature superconductivity at 65 K in single-layer FeSe films
Shaolong He;Junfeng He;Wenhao Zhang;Wenhao Zhang;Lin Zhao.
Nature Materials (2013)
Electronic origin of high-temperature superconductivity in single-layer FeSe superconductor
Defa Liu;Wenhao Zhang;Wenhao Zhang;Daixiang Mou;Junfeng He.
Nature Communications (2012)
Particle Swarm Optimization With an Aging Leader and Challengers
Wei-Neng Chen;Jun Zhang;Ying Lin;Ni Chen.
IEEE Transactions on Evolutionary Computation (2013)
An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements
Wei-Neng Chen;Jun Zhang.
systems man and cybernetics (2009)
A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems
Wei-Neng Chen;Jun Zhang;H.S.H. Chung;Wen-Liang Zhong.
IEEE Transactions on Evolutionary Computation (2010)
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches
Zhi-Hui Zhan;Xiao-Fang Liu;Yue-Jiao Gong;Jun Zhang.
ACM Computing Surveys (2015)
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
Jun Zhang;Henry Shu-Hung Chung;Wai-Lun Lo.
IEEE Transactions on Evolutionary Computation (2007)
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