Author:

Keywords:

hyper-heuristics, HyFlex, ITEC, iMinds, Science & Technology, Technology, Engineering, Manufacturing, Operations Research & Management Science, Engineering, Hyper-heuristics, general problem solver, HyFlex, ALGORITHM, 0102 Applied Mathematics, 0103 Numerical and Computational Mathematics, 1503 Business and Management, Operations Research, 3507 Strategy, management and organisational behaviour, 4901 Applied mathematics

Abstract:

This study provides a new hyper-heuristic design using a learning-based heuristic selection mechanism together with an adaptive move acceptance criterion The selection process was supported by an online heuristic subset selection strategy In addition, a pairwise heuristic hybridization method was designed The motivation behind building an intelligent selection hyper-heuristic using these adaptive hyper-heuristic sub-mechanisms is to facilitate generality Therefore, the designed hyper-heuristic was tested on a number of problem domains defined in a high-level framework, i.e.; HyFlex The framework provides a set of problems with a number of instances as well as a group of low-level heuristics Thus, it can be considered a good environment to measure the generality level of selection hyper-heuristics The computational results demonstrated the generic performance of the proposed strategy in comparison with other tested hyper-heuristics composed of the sub-mechanisms from the literature Moreover, the performance and behavior analysis conducted for the hyper-heuristic clearly showed its adaptive characteristics under different search conditions The principles comprising the here presented algorithm were at the heart of the algorithm that won the first international cross-domain heuristic search competition © 2012 Springer Science+Business Media New York.