  In order to better understand and analyze the currently widely used
population-based metaheuristic optimization algorithms, , this paper proposes a
novel computational intelligence algorithm called bare bones grey wolf
optimizer (BBGWO) inspired by a bare bones mechanism. In the BBGWO, the complex
updating mechanism of solutions is replaced by a random vector that obeys a
normal distribution, whose mean and variance are derived by theoretically
studying the probability distribution of the new solution of the original GWO.
The corresponding theoretical analysis and simulation results verify the good
optimization performance of the proposed BBGWO algorithm.
