Evolutionary optimization approach based on heuristic information with pseudo-utility for the quadratic assignment problem

Youcong Ni, Wentao Liu, Xin Du, Ruliang Xiao, Gaolin Chen, Yong Wu

Abstract: Existing data mining-based meta-heuristic approaches have endeavored to efficiently solve the Quadratic Assignment Problem (QAP). However, their captured heuristic information lacks com prehensiveness and usefulness, as they fail to consider various combinations of frequent items and ignore objective information. This deficiency makes it challenging to generate new solutions that balance global and local search. To address these issues, we propose an approach called EOHIPU. EOHIPU efficiently mines and represents more itemsets composed of frequent items, and simultaneously introduces pseudo-utility to quantitatively characterize each item's contribution to the objective, thereby capturing more comprehensive and useful heuristic information. With this heuristic information, EOHIPU designs a mechanism for generating new solutions, balancing global and local search. We conduct experiments on 21 hard benchmark instances from QAPLIB, comparing EOHIPU with five state-of-the-art approaches. Our EOHIPU achieves the best results, with average percentage deviations (APDs) of 0.064 and 0.046 under short and long running times, respectively. Additionally, EOHIPU demonstrates faster convergence compared to other state-of-the-art approaches across all instances. These results highlight EOHIPU's superior solution quality and convergence rate. Moreover, the effectiveness of the captured heuristic information and the new solution generation mechanism is validated.