Real-world optimization problems often consist of several NP-hard combinatorial optimization problems that interact with each other.
Such multi-component optimization problems are difficult to solve not only because of the contained hard optimization problems,
but in particular, because of the interdependencies between the different components. Interdependence complicates a decision making
by forcing each sub-problem to influence the quality and feasibility of solutions of the other sub-problems. This influence might be
even stronger when one sub-problem changes the data used by another one through a solution construction process. Examples of multi-component
problems are vehicle routing problems under loading constraints
the maximizing material utilization while respecting a production schedule
the relocation of containers in a port while minimizing idle times of ships, and
the traveling thief problem
The goal of this special session is to provide a forum for researchers in computational intelligence working on multi-component
optimization problems. While the main focus of this session is evolutionary computation, other approaches as well as combinations with
fuzzy systems or neural networks are highly welcome, too. The scope of this special session is very broad and includes all topics
related to multi-component problems.
Topics include (but are not limited to):
- Applications of evolutionary algorithms and swarm intelligence methods to multi-component problems
- Benchmark design
- Hybrid approaches (including fuzzy systems and neural networks) for multi-component problems
- Industrial applications
- Theoretical investigations
You should follow the IEEE CEC 2014 submission website
On the submission system you must select
"SS23. EC23: Heuristic Methods for Multi-Component Optimization Problems" as "Main Research Topic".
Special session papers are treated in the same way as regular conference papers.