A New Cooperative Framework for Parallel Trajectory-Based Metaheuristics

TL;DR

This paper introduces the PEB framework for parallel trajectory-based metaheuristics, demonstrating its effectiveness through a parallel Guided Local Search variant on the TSP, showing improved performance on supercomputers.

Contribution

The paper proposes the PEB framework for parallel metaheuristics and designs PEBGLS, a novel parallel Guided Local Search, validated through extensive supercomputer experiments.

Findings

PEBGLS performs competitively on the TSP.

The PEB framework effectively guides parallel search processes.

Experimental results confirm the framework's usefulness.

Abstract

In this paper, we propose the Parallel Elite Biased framework (PEB framework) for parallel trajectory-based metaheuristics. In the PEB framework, multiple search processes are executed concurrently. During the search, each process sends its best found solutions to its neighboring processes and uses the received solutions to guide its search. Using the PEB framework, we design a parallel variant of Guided Local Search (GLS) called PEBGLS. Extensive experiments have been conducted on the Tianhe-2 supercomputer to study the performance of PEBGLS on the Traveling Salesman Problem (TSP). The experimental results show that PEBGLS is a competitive parallel metaheuristic for the TSP, which confirms that the PEB framework is useful for designing parallel trajectory-based metaheuristics.