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Recent Advances in Population–Based Real-Parameter Optimization Algorithms

Ponnuthurai Nagaratnam Suganthan
Associate Professor Nanyang Technological University



Population based real-parameter optimization algorithms have been investigated over the last five decades. Evolution strategy is one of the earliest. Subsequently, several variants of real-coded GAs were developed. Even though methods such as the differential evolution, CMA-ES and particle swarm optimizer have dominated the single objective numerical optimization field, vast majority of recent swarm algorithms were also primarily developed to solve numerical optimization problems. In addition, it is also true that there are a vast number of real-world numerical optimization problems. Further, real-parameter population based algorithms with some modifications have also been extensively incorporated in the solution procedures of combinatorial problems due to the superior exploratory capabilities of real-parameter population based algorithms.

This tutorial will commence with a historical look at the population-based algorithms and with a brief introduction to various classifications of numerical optimization problems (such as static, dynamic, large scale, constrained, multi-objective, multimodal, etc). The tutorial will then present several population-based numerical optimization algorithms such as CMA-ES, real-coded GAs, differential evolution, particle swarm optimization, invasive weeds, harmony search, etc. The tutorial will also elaborate on their variants for solving diverse problem scenarios such as constrained optimization, multi-objective optimization, multimodal optimization, etc. Finally, the tutorial will briefly highlight some applications and future research directions.

Expected enrolment:

The vast majority of population-based optimization algorithms were developed primarily for solving real parameter optimization. Recently, numerous swarm intelligence based algorithms have also been introduced. These algorithms have not been well tested on many problem scenarios such as constrained optimization, multi-objective optimization, large scale, multimodal optimization, etc. thereby offering immense opportunities for research and exploration. Hence, this tutorial will be valuable to all researchers interested in numerical optimization.



Ponnuthurai Nagaratnam Suganthan received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively. He obtained his Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. He was a predoctoral Research Assistant in the Dept of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Dept of Computer Science and Electrical Engineering, University of Queensland in 1996–99. Since 1999 he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore where he was an Assistant Professor and now is an Associate Professor. He is an Editorial Board Member of the Evolutionary Computation Journal, MIT Press. He is an associate editor of the IEEE Trans on Evolutionary Computation, IEEE Trans on Cybernetics, Information Sciences (Elsevier), Pattern Recognition (Elsevier) and Int. J. of Swarm Intelligence Research Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation, an Elsevier Journal. His co-authored SaDE (April 2009) paper won "IEEE Trans. on Evolutionary Computation" outstanding paper award. He has co-organized numerous competitions on numerical optimization at CECs since 2005. These competitions and associated technical reports have been well received by the community. His research interests include evolutionary computation, pattern recognition, multi-objective evolutionary algorithms, bioinformatics, applications of evolutionary computation and neural networks. His publications have been well cited (Googlescholar Citations). He is a Senior Member of the IEEE. Homepage: http://www.ntu.edu.sg/home/epnsugan/


The length of the tutorial:
two hours.


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