  In this work, we propose a hybrid parallel Jaya optimisation algorithm for a
multi-core environment with the aim of solving large-scale global optimisation
problems. The proposed algorithm is called HHCPJaya, and combines the
hyper-population approach with the hierarchical cooperation search mechanism.
The HHCPJaya algorithm divides the population into many small subpopulations,
each of which focuses on a distinct block of the original population
dimensions. In the hyper-population approach, we increase the small
subpopulations by assigning more than one subpopulation to each core, and each
subpopulation evolves independently to enhance the explorative and exploitative
nature of the population. We combine this hyper-population approach with the
two-level hierarchical cooperative search scheme to find global solutions from
all subpopulations. Furthermore, we incorporate an additional updating phase on
the respective subpopulations based on global solutions, with the aim of
further improving the convergence rate and the quality of solutions. Several
experiments applying the proposed parallel algorithm in different settings
prove that it demonstrates sufficient promise in terms of the quality of
solutions and the convergence rate. Furthermore, a relatively small
computational effort is required to solve complex and large-scale optimisation
problems.
