A Hierarchical Social Metaheuristic for the Max-Cut Problem

This paper introduces a new social metaheuristic for the Max-Cut problem applied to a weighted undirected graph. This problem consists in finding a partition of the nodes into two subsets, such that the sum of the weights of the edges having endpoints in different subsets is maximized. This NP-hard problem for non planar graphs has several application areas such as VLSI and ASICs CAD. The hierarchical social (HS) metaheuristic here proposed for solving the referred problem is tested and compared with other two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and an hybrid memetic heuristic that combines a genetic algorithm (GA) and a local search. The computational results on a set of standard test problems show the suitability of the approach.

• Type: Conference
• Year: 2004
• Where: EVOW
• Authors: Abraham Duarte, Felipe Fernández, Ángel Sánchez, Antonio Sanz
• Keywords: Artificial Intelligence, EVOW 2004, Greedy Randomized Adaptive Search Procedure, Max-Cut Problem, Weighted Undirected Graph