The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance

Abstract

Metaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ performance. In this paper the parameterization procedure of Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) is addressed, with a focus on the Neighborhood Structure (NS). Numerous NS have been proposed for specific problems, which seem to indicate that the NS is a special parameter, whose optimization is independent of other parameters. The performance of eight NS was examined with SA and DABC under two optimization constraints, regarding computational time variation, to determine if there is one appropriate NS for the TSP problem, independent of the rest of the parameters of the optimization procedure. The computational study carried out for comparing the evaluation of the NS, including a statistical analysis, demonstrated a nonproportional increase in the performance of DABC with some NS. For SA the improvement of the solutions appeared to be more uniform with an almost nonexistent variance in improvement.

A Self-Parametrization Framework for Meta-Heuristics

“…Moreover, in other to ensure both the proposed framework and the conventionally parametrized DABC have found suitable solutions, SA was also included in the computational study. SA's resilience to subpar parametrization is well-known [71][72][73]. Since SA is included as a baseline and it will not be included in the statistical comparison, its parametrization process will not be described.…”