P
Ponnuthurai Nagaratnam Suganthan
Researcher at Nanyang Technological University
Publications - 504
Citations - 52231
Ponnuthurai Nagaratnam Suganthan is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Optimization problem & Evolutionary algorithm. The author has an hindex of 91, co-authored 451 publications receiving 42332 citations. Previous affiliations of Ponnuthurai Nagaratnam Suganthan include Kobe University & National Centre for Biological Sciences.
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Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
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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.
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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.
Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization
Ponnuthurai Nagaratnam Suganthan,Nikolaus Hansen,Jing Liang,Kalyanmoy Deb,Y. P. Chen,Anne Auger,Santosh Tiwari +6 more
TL;DR: This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions.
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Multiobjective evolutionary algorithms: A survey of the state of the art
TL;DR: This paper surveys the development ofMOEAs primarily during the last eight years and covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEas, coevolutionary MOE As, selection and offspring reproduction operators, MOE as with specific search methods, MOeAs for multimodal problems, constraint handling and MOE