MT4HJMath 2016 - Special Issue on Metaheuristics and Hybrid Methods for Combinatorial Optimization Problems

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 issue 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