IEEE Symposium on Differential Evolution (IEEE SDE)

IEEE SSCI 2020 will run as a virtual conference.

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 Xiamen, China, and beyond.

Topics

• 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
• Ensemble approaches in 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 real-world problems
• DE for interactive optimization

Symposium Chairs

Rammohan Mallipeddi

mallipeddi@knu.ac.kr

School of Electronics Engineering, Kyungpook National University, South Korea

Guohua Wu

guohuawu@csu.edu.cn

School of Traffic and Transportation Engineering, Central South University, Changsha, China

Kenneth V. Price

kvprice@pacbell.net

Vacaville, USA