  We introduced the Scorpion Hunting Strategy (SHS), a novel population-based,
nature-inspired optimisation algorithm. This algorithm draws inspiration from
the hunting strategy of scorpions, which identify, locate, and capture their
prey using the alpha and beta vibration operators. These operators control the
SHS algorithm's exploitation and exploration abilities. To formulate an
optimisation method, we mathematically simulate these dynamic events and
behaviors. We evaluate the effectiveness of the SHS algorithm by employing 20
benchmark functions (including 10 conventional and 10 CEC2020 functions), using
both qualitative and quantitative analyses. Through a comparative analysis with
12 state-of-the-art meta-heuristic algorithms, we demonstrate that the proposed
SHS algorithm yields exceptionally promising results. These findings are
further supported by statistically significant results obtained through the
Wilcoxon rank sum test. Additionally, the ranking of SHS, as determined by the
average rank derived from the Friedman test, positions it at the forefront when
compared to other algorithms. Going beyond theoretical validation, we showcase
the practical utility of the SHS algorithm by applying it to six distinct
real-world optimisation tasks. These applications illustrate the algorithm's
potential in addressing complex optimisation challenges. In summary, this work
not only introduces the innovative SHS algorithm but also substantiates its
effectiveness and versatility through rigorous benchmarking and real-world
problem-solving scenarios.
