2022 - Research.com Computer Science in Singapore Leader Award
2015 - IEEE Fellow For contributions to optimization using evolutionary and swarm algorithms
Ponnuthurai Nagaratnam Suganthan focuses on Mathematical optimization, Evolutionary algorithm, Artificial intelligence, Evolutionary computation and Benchmark. His study in Differential evolution, Optimization problem, Particle swarm optimization, Multi-swarm optimization and Local search is done as part of Mathematical optimization. His research in the fields of IEEE Congress on Evolutionary Computation overlaps with other disciplines such as Continuous parameter.
His Evolutionary algorithm study integrates concerns from other disciplines, such as Genetic algorithm, Algorithm, Search algorithm, Continuous optimization and Generalization error. Ponnuthurai Nagaratnam Suganthan has researched Artificial intelligence in several fields, including Machine learning and Data mining. His biological study spans a wide range of topics, including Multi-objective optimization, Swarm intelligence, Selection and Crossover.
Ponnuthurai Nagaratnam Suganthan mainly investigates Mathematical optimization, Artificial intelligence, Optimization problem, Differential evolution and Evolutionary algorithm. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Algorithm, Job shop scheduling and Benchmark. His Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
His work deals with themes such as Mutation, Global optimization and Crossover, which intersect with Differential evolution. His Evolutionary algorithm research incorporates elements of Genetic algorithm, Constraint and Search algorithm. His Evolutionary computation study incorporates themes from Algorithm design and Premature convergence.
Ponnuthurai Nagaratnam Suganthan mainly focuses on Mathematical optimization, Optimization problem, Artificial intelligence, Evolutionary algorithm and Differential evolution. His Mathematical optimization study combines topics from a wide range of disciplines, such as Electric power system, Crossover and Benchmark. His study in Optimization problem is interdisciplinary in nature, drawing from both Covariance matrix, Particle swarm optimization, Metaheuristic and Surrogate model.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition. His work carried out in the field of Evolutionary algorithm brings together such families of science as Subspace topology, Pareto principle, Selection, Cultural algorithm and Algorithm. The Differential evolution study combines topics in areas such as Control theory, Nonlinear system, Estimation theory, Reduction and Search algorithm.
Mathematical optimization, Optimization problem, Evolutionary algorithm, Differential evolution and Artificial intelligence are his primary areas of study. His work deals with themes such as AC power, Electric power system and Benchmark, which intersect with Mathematical optimization. Ponnuthurai Nagaratnam Suganthan interconnects No free lunch in search and optimization and Crossover in the investigation of issues within Optimization problem.
His Differential evolution research includes themes of Evolutionary computation, Maximum power principle and Search algorithm. His research in Evolutionary computation intersects with topics in Rate of convergence, Premature convergence, Global optimization and Heuristic. His Artificial intelligence study incorporates themes from Swarm intelligence, Machine learning and Pattern recognition.
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Differential Evolution: A Survey of the State-of-the-Art
S Das;P N Suganthan.
IEEE Transactions on Evolutionary Computation (2011)
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
J.J. Liang;A.K. Qin;P.N. Suganthan;S. Baskar.
IEEE Transactions on Evolutionary Computation (2006)
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
A.K. Qin;V.L. Huang;P.N. Suganthan.
IEEE Transactions on Evolutionary Computation (2009)
Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization
P. N. Suganthan;N. Hansen;J. J. Liang;K. Deb.
Multiobjective evolutionary algorithms: A survey of the state of the art
Aimin Zhou;Bo-Yang Qu;Hui Li;Shi-Zheng Zhao.
Swarm and evolutionary computation (2011)
Particle swarm optimiser with neighbourhood operator
congress on evolutionary computation (1999)
Self-adaptive differential evolution algorithm for numerical optimization
A.K. Qin;P.N. Suganthan.
congress on evolutionary computation (2005)
Differential evolution algorithm with ensemble of parameters and mutation strategies
R. Mallipeddi;P. N. Suganthan;Q. K. Pan;M. F. Tasgetiren.
Applied Soft Computing (2011)
Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization
J. J. Liang;B. Y. Qu;P. N. Suganthan.
Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization
K. Tang;X. Yao;P. N. Suganthan;C. MacNish.
Swarm and Evolutionary Computation
(Impact Factor: 10.267)
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