  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.
