2017 IEEE Symposium on Differential Evolution (IEEE SDE' 17)

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 2017 in Hawaii, USA and beyond.

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Authors are invited to submit their original and unpublished work in the areas including (but not limited to) the following: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.