CFP: IEEE CEC 2019 Special Session on Pigeon-Inspired Optimization

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.

Aim and Scope

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Authors are invited to submit their original and unpublished work to this special session.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:

• Analysis and control of PIO parameters
• Parallelized and distributed realizations of PIO algorithms
• PIO for Multi-objective optimization
• PIO for Constrained optimization
• PIO for Discrete optimization
• PIO algorithm with data mining techniques
• PIO in uncertain environments
• Theoretical aspects of PIO algorithm
• PIO for Real-world applications

Important dates

• Paper submission: 7 January, 2019
• Decision notification: 7 March, 2019
• Camera ready paper due: 31 March, 2019
• Registration: 31 March, 2019
• Conference: 10 June, 2019

Note: all deadlines are 11:59pm US pacific time.

Paper Submission

Please follow the IEEE CEC2019 Submission Web Site. Special session papers are treated the same as regular conference papers. Please specify that your paper is submitted to Pigeon-Inspired Optimization (PIO). All papers accepted and presented at CEC2019 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.

Organisers

Haibin Duan - School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China.
hbduan@buaa.edu.cn
Phone: +86-10-8231-7318; Fax: +86-10- 8232-8116.

Yin WANG - College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
yinwangee@nuaa.edu.cn
Phone: +86-25-8489-2805.