Differential Evolution (DE) is arguably one
of the most powerful stochastic real-parameter
optimization algorithms in current use. DE is a very
simple algorithm, requiring only a few lines of code in
most of the existing programming languages. Additionally,
it has very few control parameters. Nonetheless, DE exhibits
remarkable performance in optimizing a wide variety of optimization
problems in terms of final accuracy, convergence speed, and robustness as
evidenced by the consistently excellent performance in all of the CEC competitions
(http://www3.ntu.edu.sg/home/epnsugan). The last decade has witnessed a rapidly
growing research interest in DE as demonstrated by the significant increase in the number
of research publications on DE in the forms of monographs, edited volumes, and archival
articles. Although research on and with DE has reached an impressive state, there are still many
open problems and new application areas are continually emerging for the algorithm and its variants.
This Symposium aims at bringing researchers and users from academia and industry together to report,
interact and review the latest progress in this field, to explore future directions of research and to
publicize DE to a wider audience from diverse fields joining the IEEE SSCI in Bengaluru, India, and
beyond.
The manuscripts should be submitted in PDF format. Click Here to know further guidelines for submission.
Topics
Authors are invited to submit their original and unpublished work in the areas including (but not limited to) the following:
Theoretical analysis of the search mechanism, complexity of DE- Adaptation and tuning of the control parameters of DE
- Development of new vector perturbation techniques for DE
- Adaptive mixing of the perturbation techniques
- Balancing explorative and exploitative tendencies in DE and memetic DE
- DE for finding multiple global optima
- DE for noisy and dynamic objective functions
- DE for multi-objective optimization
- Robust DE Variants
- Rotationally Invariant DE
- Constraints handling with DE
- DE for high-dimensional optimization
- DE-variants for handling mixed-integer, discrete, and binary optimization problems
- Hybridization of DE with other search methods
- Hybridization with Paradigms such as Neuro-fuzzy, Statistical Learning, Machine Learning, etc.
- Development of challenging problem sets for DE
- Applications of DE in any domain.
Symposium Co-Chairs
Janez Brest
University of Maribor, Maribor.
E-mail: [email protected]
Swagatam Das
Indian Statistical Institute, Kolkata-700 108, India
E-mail: [email protected]
Ferrante Neri
De Montfort University, UK.
E-mail: [email protected]
Program Committee
(To be announced)