**Special
Session:**

**Metaheuristics**** and Hybrid Methods for Combinatorial
Optimization Problems**

**at**** the IEEE SSCI 2016 CISND Symposium**

**Organisers****:** Jingpeng
Li (jli@cs.stir.ac.uk), Rong Qu and Yindong Shen

**Aims and scopes**

Optimization problems can be divided
into two categories: the first category consists of problems with continuous
variables and the second category consists of problems with discrete variables.
Among the latter ones, there are a class of problems called combinatorial
optimization problems, in which we are looking for the best possible solution
from a finite set of discrete decision variables subject to a set of
constraints among variables, and this solution may typically be an integer
number, a permutation, a subset, or a graph structure.

Combinatorial optimization has
important applications in various fields including computer science,
management, and engineering. Many such problems (e.g., traveling salesman
problems, maximum satisfiability problems,
timetabling problems, and scheduling and rostering
problems) cannot be solved exactly within reasonable time limits due to the
problem instance sizes of practical interest. To achieve a trade-off between
solution quality and search completeness, metaheuristic
approaches have therefore been widely studied and can be applied, with suitable
modifications, to a broad class of combinatorial optimization problems. Some
well-known examples of metaheuristics include genetic
algorithms, memetic algorithms, ant colony
optimization, estimation of distribution algorithms,
particle swarm optimisation, stochastic local search, GRASP, simulated
annealing, tabu search, and variable neighbourhood
search.

The purpose of this special session
is to provide a premier forum for researchers to disseminate their high quality
and original research results on all kinds of metaheuristics
for combinatorial problems either in an application perspective or from a
theoretical sense.

Potential topics include, but are
not limited to:

·
Applications of metaheuristics
to combinatorial optimization problems

·
In-depth experimental analysis and
comparisons between different techniques

·
Neighborhoods and efficient algorithms for searching them

·
Hybrid methods (e.g., memetic computing, matheuristics,
hyperheuristics)

·
Meta-analytics and search space
landscape analyses

·
Theoretical studies of metaheuristics

·
Representation techniques

·
Multiobjective combinatorial optimization

·
Constraint-handling techniques in metaheuristics

·
Automated tuning of metaheuristics

·
Automated design of metaheuristics

**Important dates**

·
Paper submission: August 15, 2016

·
Paper acceptance: September 12, 2016

·
Final submission: October 10, 2016

·
Early registration: October 10, 2016

**Organisers**

Dr Jingpeng
Li

Division of Computing Science &
Mathematics

University of Stirling

Stirling, UK

Dr Rong
Qu

School of Computer Science

University of Nottingham

Nottingham, UK

Prof Yindong
Shen

School of Automation

Huazhong University of Science and Technology

Wuhan, China