D-Index & Metrics Best Publications
Ponnuthurai Nagaratnam Suganthan

Ponnuthurai Nagaratnam Suganthan

Computer Science
Singapore
2022

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 88 Citations 37,825 320 World Ranking 285 National Ranking 6

Research.com Recognitions

Awards & Achievements

2022 - Research.com Computer Science in Singapore Leader Award

2015 - IEEE Fellow For contributions to optimization using evolutionary and swarm algorithms

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Mathematical optimization

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.

His most cited work include:

  • Differential Evolution: A Survey of the State-of-the-Art (3076 citations)
  • Comprehensive learning particle swarm optimizer for global optimization of multimodal functions (2468 citations)
  • Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization (2327 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Mathematical optimization (48.75%)
  • Artificial intelligence (33.11%)
  • Optimization problem (23.13%)

What were the highlights of his more recent work (between 2016-2021)?

  • Mathematical optimization (48.75%)
  • Optimization problem (23.13%)
  • Artificial intelligence (33.11%)

In recent papers he was focusing on the following fields of study:

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.

Between 2016 and 2021, his most popular works were:

  • Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting (190 citations)
  • Bio-inspired computation: Where we stand and what's next (172 citations)
  • Ensemble of differential evolution variants (146 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Differential Evolution: A Survey of the State-of-the-Art

S Das;P N Suganthan.
IEEE Transactions on Evolutionary Computation (2011)

3917 Citations

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)

3112 Citations

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)

2865 Citations

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.
(2005)

2169 Citations

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)

1619 Citations

Particle swarm optimiser with neighbourhood operator

P.N. Suganthan.
congress on evolutionary computation (1999)

1237 Citations

Self-adaptive differential evolution algorithm for numerical optimization

A.K. Qin;P.N. Suganthan.
congress on evolutionary computation (2005)

1175 Citations

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)

1025 Citations

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.
(2014)

922 Citations

Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization

K. Tang;X. Yao;P. N. Suganthan;C. MacNish.
(2008)

875 Citations

Editorial Boards

Swarm and Evolutionary Computation
(Impact Factor: 10.267)

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