Evolving Self-Adaptive Tabu Search Algorithm for Storage Location Assignment Problems

Jing Xie, Yi Mei, Andy Song

Abstract: This study proposes a novel grammar guided Genetic Programming method to solve a real world problem, the Storage Location Assignment Problem (SLAP) with Grouping Constraints. Self-adaptive Tabu Search algorithms are evolved by this approach and it can be used as solvers for SLAPs. A novel self-adaptive Tabu Search framework is proposed that key configurations of the algorithm are determined based on the problem-specific characters, and these configurations are changed dynamically during the search process. In addition, both the quality of the solutions and the execution speed are considered in the evaluation function. The experimental results show that more efficient Tabu Search algorithms can be found by this approach comparing to a manually-designed Tabu Search method.