Wellington, New Zealand, 10-13 June 2019
Swarm intelligence algorithm should have two kinds of ability: capability learning and capacity developing. The Pigeon-Inspired Optimization (PIO) algorithm is a new kind of swarm intelligence, which is based on the behaviors of homing pigeons. It is natural to expect that an optimization algorithm based on pigeons could be a better optimization algorithm than existing swarm intelligence algorithms which are based on collective behavior of simple insects, because pigeons have strong individual and social ability. The designed optimization algorithm will naturally have the capability of both convergence and divergence.
The PIO algorithm is a good example of developmental swarm intelligence algorithm. A “good enough” optimum could be obtained through solution divergence and convergence in the search space. In the PIO algorithm, the process of optimization is considered to be the homing of pigeons. the homing pigeons can easily find their home with the aid of three homing tools: the magnetic field, the sun and the landmarks. Pigeons rely more on map and compass-like information at the beginning of the journey. Landmarks provide more information to pigeons in the midway. Moreover, the route is evaluated and revised timely to guarantee that they can reach the destination through the optimal route. Inspired by these facts, two operators are introduced in the PIO algorithm, i.e., the map and compass operator and the landmark operator.
The PIO algorithm is a combination of swarm intelligence and data mining techniques. Every pigeon optimization algorithm is not a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.
This special session aims at presenting the latest developments of PIO algorithm, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to algorithms are very welcome for this Special Session.
Authors are invited to submit their original and unpublished work to this special session. Topics of interest include but are not limited to:
Haibin Duan School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China. email@example.com Phone: +86-10-8231-7318; Fax: +86-10- 8232-8116.
Yin WANG College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China. firstname.lastname@example.org Phone: +86-25-8489-2805.
Haibin Duan is currently a professor with the School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China. He received the Ph.D. degree from Nanjing University of Aeronautics and Astronautics (NUAA) in 2005. He was once an engineer of Shenyang Aircraft Design Research Institute of AVIC, and a technician of China Aviation Motor Control System Institute. His research focuses on multiple UAS autonomous formation control, and biological computer vision. He has led a number of national or military projects and been invited to give speeches or keynote speeches in many major academic conferences. He has authored or coauthored more than 70 papers in journals listed in the Science Citation Index. He is the inventor of Pigeon-Inspired Optimization (PIO) algorithm. There are 27 patents in his name. He is a senior member of IEEE, a member of IFAC TC7.5.
Prof. Duan was the recipient of the National Science Fund for Distinguished Young Scholars in 2014, and he was also the recipient of the 13th China Youth Science and Technology Award, the 19th National Youth Five-Four Medal Award, the 5th Yang Jia-Chi Science and Technology Award, the 16th MAO Yi-sheng Beijing Youth Science and Technology Award, the 12th Youth Science and Technology Award of Chinese Association of Aeronautics and Astronautics, the 1st Class of the 3rd WU Wen-Tsun Artificial Intelligence Science and Technology Award, the 1st Young Scientist Award of Chinese Association of Automation, and the 27th Beijing Youth Medal Award. He was nominated as one of the excellent researchers of the Young Leading Talents of Science and Technology Innovation Program of the Ten Thousand Talents Program of China, Young Leading Talents of Science and Technology Innovation of Ministry of Science and Technology, Top-notch Young Talents Program of the Ten Thousand Talents Program of China, Program for New Century Excellent Talents in University of Ministry of Education of China, and Beijing NOVA Program, in 2017, 2016, 2013, 2010 and 2007, respectively.
Yin WANG received the B.S. degree in Electrical Engineering & Electronics from Nanjing University of Aeronautics and Astronautics, China, in 2008, and Ph.D. degree in Information Engineering from City University London in 2011 (with full research studentship 1/11). He then became a lecturer in Nanjing University of Aeronautics and Astronautics, and was promoted as associate professor in 2016. He is the director of undergraduate program in aircraft informatics and control engineering. His research interests include pattern recognition, image processing and computer vision, neural and evolutionary systems with applications in aeronautical applications and signal processing. His research is funded by the National Sciences Research Council of China and the department of aerospace industries.