The course timetabling problem is one of the most difficult combinatorial problems, it requires the assignment of a fixed number of subjects into a number of time slots minimizing the number of student conflicts. This article presents a comparison between state-of-the-art hyper-heuristics and a newly proposed iterated variable neighborhood descent algorithm when solving the course timetabling p...

This paper presents an adaptive neighborhood search method (ANS) forsolving the nurse rostering problem proposed for the First InternationalNurse Rostering Competition (INRC-2010). ANS uses jointly two distinctneighborhood moves and adaptively switches among three intensificationand diversification search strategies according to the search history. Com-putational results ass...

Large-scale multi-objective optimization problems (LS-MOP) are complex problems with a large number of decision variables. Due to its high-dimensional decision space, LS-MOP poses a significant challenge to multi-objective optimization methods including multiobjective evolutionary algorithms (MOEAs). Following the algorithmic framework of multiobjective evolutionary algorithm based on decomposi...