C
Changhe Li
Researcher at China University of Geosciences (Wuhan)
Publications - 118
Citations - 4361
Changhe Li is an academic researcher from China University of Geosciences (Wuhan). The author has contributed to research in topics: Optimization problem & Evolutionary algorithm. The author has an hindex of 25, co-authored 90 publications receiving 3351 citations. Previous affiliations of Changhe Li include University of Leicester.
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Journal ArticleDOI
A survey of swarm intelligence for dynamic optimization: Algorithms and applications
TL;DR: A broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications, and some considerations about future directions in the subject are given.
Journal ArticleDOI
Diversity enhanced particle swarm optimization with neighborhood search
TL;DR: A hybrid PSO algorithm is proposed, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities.
Journal ArticleDOI
A Self-Learning Particle Swarm Optimizer for Global Optimization Problems
TL;DR: A novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems, which can enable a particle to choose the optimal strategy according to its own local fitness landscape.
Journal ArticleDOI
A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments
Shengxiang Yang,Changhe Li +1 more
TL;DR: The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.
Proceedings ArticleDOI
Opposition-based particle swarm algorithm with cauchy mutation
TL;DR: An Opposition-based PSO (OPSO) to accelerate the convergence of PSO and avoid premature convergence is presented, which employs opposition-based learning for each particle and applies a dynamic Cauchy mutation on the best particle.