Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem

Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces.

Automated summary

Search Trajectory Networks (STNs) are introduced as a tool for analyzing the behavior of metaheuristics in relation to their exploration ability, focusing on a specific combinatorial optimization problem. Two algorithms are analyzed using STNs for the cyclic bandwidth sum minimization problem, and a novel grouping method is proposed for both continuous and combinatorial spaces.