  In management, business, economics, science, engineering, and research
domains, Large Scale Global Optimization (LSGO) plays a predominant and vital
role. Though LSGO is applied in many of the application domains, it is a very
troublesome and a perverse task. The Congress on Evolutionary Computation (CEC)
began an LSGO competition to come up with algorithms with a bunch of standard
benchmark unconstrained LSGO functions. Therefore, in this paper, we propose a
hybrid meta-heuristic algorithm, which combines an Improved and Modified
Harmony Search (IMHS), along with a Modified Differential Evolution (MDE) with
an alternate selection strategy. Harmony Search (HS) does the job of
exploration and exploitation, and Differential Evolution does the job of giving
a perturbation to the exploration of IMHS, as harmony search suffers from being
stuck at the basin of local optimal. To judge the performance of the suggested
algorithm, we compare the proposed algorithm with ten excellent meta-heuristic
algorithms on fifteen LSGO benchmark functions, which have 1000 continuous
decision variables, of the CEC 2013 LSGO special session. The experimental
results consistently show that our proposed hybrid meta-heuristic performs
statistically on par with some algorithms in a few problems, while it turned
out to be the best in a couple of problems.
