Diversification-based learning simulated annealing algorithm for hub location problems

Abstract

Purpose

The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems.

Design/methodology/approach

This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets.

Findings

This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates.

Practical implications

Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time.

Originality/value

To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

Citation

Rathore, H., Nandi, S., Pandey, P. and Singh, S.P. (2019), "Diversification-based learning simulated annealing algorithm for hub location problems", Benchmarking: An International Journal, Vol. 26 No. 6, pp. 1995-2016. https://doi.org/10.1108/BIJ-04-2018-0092