Path Relinking Scheme for the Max-Cut Problem

In this chapter, a path relinking method for the maximum cut problem is investigated. The authors consider an implementation of the path-relinking, where it is utilized as a subroutine for another meta-heuristic search procedure. Particularly, the authors focus on the global equilibrium search method to provide a set of high quality solutions, the set that is used within the path relinking method. The computational experiment on a set of standard benchmark problems is provided to study the proposed approach. The authors show that when the size of the solution set that is passed to the path relinking procedure is too large, the resulting running times follow the restart distribution, which guarantees that an underlying algorithm can be accelerated by removing all of the accumulated data (set P) and re-initiating its execution after a certain number of elite solutions is obtained.

Path Relinking Scheme for the Max-Cut Problem within Global Equilibrium Search

In this paper, the potential of the path relinking method for the maximum cut problem is investigated. This method is embedded within global equilibrium search to utilize the set of high quality solutions provided by the latter. The computational experiment on a set of standard benchmark problems is provided to study the proposed approach. The empirical experiments reveal that the large sizes of the elite set lead to restart distribution of the running times, i.e., the algorithm can be accelerated by simply removing all of the accumulated data (set P) and re-initiating its execution after a certain number of elite solutions is obtained.

Path Relinking Scheme for the Max-Cut Problem within Global Equilibrium Search

In this paper, the potential of the path relinking method for the maximum cut problem is investigated. This method is embedded within global equilibrium search to utilize the set of high quality solutions provided by the latter. The computational experiment on a set of standard benchmark problems is provided to study the proposed approach. The empirical experiments reveal that the large sizes of the elite set lead to restart distribution of the running times, i.e., the algorithm can be accelerated by simply removing all of the accumulated data (set P) and re-initiating its execution after a certain number of elite solutions is obtained.