Special Session:

Metaheuristics and Hybrid Methods for Combinatorial Optimization Problems

at the IEEE SSCI 2016 CISND Symposium


Organisers: Jingpeng Li (, 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


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