Organizers:
Keiki Takadama (The University of Electro-Communications, Japan),
Shio Kawakami (The University of Electro-Communications, Japan),
Hiroyuki Sato (The University of Electro-Communications, Japan)

Contact email:
cec2024-ecsi@hs.hc.uec.ac.jp

Website:
https://sites.google.com/gl.cc.uec.ac.jp/cec2024-ecsi/

Scope and Topics:
Many evolutionary computations (ECs) and swarm intelligence (SI) succeed in optimization in the
“static” environment where the optimal solutions or the landscape of solutions are/is fixed in given
single and multi-objective functions. However, EC/SI has not yet established in the “dynamic”
environment where the optimal solutions or the landscape of solutions change(s) with lapse of time.
In such an environment, new methods are needed to adapt to the changing landscape. What should
be noted here is that these kinds of techniques are useful not only for “dynamical environment
optimization” but also for “(evolutionary) multitask optimization” which solves multiple tasks
simultaneously. This is because (i) the group dynamics of individuals (e.g., a ratio of individuals of
each task) changes in a process of solving multiple tasks and (ii) the individuals should adapt to a
group dynamics change to solve multiple tasks effectively in the multitask optimization as well as
the individuals should adapt to a landscape change to track the changing optimal solutions in the
dynamical environment optimization. From these similar characteristics of “dynamical environment
optimization” and “multitask optimization”, this special session aims at bringing together
researchers from both research areas to explore new methods of EC/SI for dynamical environments
and multitasking problems, and explore future directions in this field.
The topics of this special session include but are not limited to the following topics:

- Evolutionary computations (EC) for dynamic single or multiple objective function
- Swarm intelligence (SI) for dynamic single or multiple objective function
- EC/SI for dynamic multimodal functions
- Evolutionary multitask optimization
- Evolutionary multi-factorial optimization
- Multitasking techniques for controlling cooperation among individuals
- Theory for adapting to “landscape change” or “group dynamics change”
- Real-world problems as dynamic environments and multitasking problems