A
A. K. Qin
Researcher at Swinburne University of Technology
Publications - 138
Citations - 12885
A. K. Qin is an academic researcher from Swinburne University of Technology. The author has contributed to research in topics: Evolutionary algorithm & Optimization problem. The author has an hindex of 32, co-authored 130 publications receiving 10313 citations. Previous affiliations of A. K. Qin include RMIT University & University of Waterloo.
Papers
More filters
Journal ArticleDOI
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
TL;DR: The comprehensive learning particle swarm optimizer (CLPSO) is presented, which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity.
Journal ArticleDOI
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
Proceedings ArticleDOI
Self-adaptive differential evolution algorithm for numerical optimization
TL;DR: A novel self-adaptive differential evolution algorithm (SaDE), where the choice of learning strategy and the two control parameters F and CR are not required to be pre-specified.
Journal ArticleDOI
Rapid and brief communication: Evolutionary extreme learning machine
TL;DR: A hybrid learning algorithm is proposed which uses the differential evolutionary algorithm to select the input weights and Moore-Penrose (MP) generalized inverse to analytically determine the output weights.
Journal ArticleDOI
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
TL;DR: A multiobjective deep belief networks ensemble (MODBNE) method that employs a multiobjectives evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives is proposed.