  This paper proposes a novel nature-inspired meta-heuristic algorithm called
the Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems. It
mimics golden tortoise beetle's behavior of changing colors to attract opposite
sex for mating and its protective strategy that uses a kind of anal fork to
deter predators. The algorithm is modeled based on the beetle's dual
attractiveness and survival strategy to generate new solutions for optimization
problems. To measure its performance, the proposed GTBO is compared with five
other nature-inspired evolutionary algorithms on 24 well-known benchmark
functions investigating the trade-off between exploration and exploitation,
local optima avoidance, and convergence towards the global optima is
statistically significant. We particularly applied GTBO to two well-known
engineering problems including the welded beam design problem and the gear
train design problem. The results demonstrate that the new algorithm is more
efficient than the five baseline algorithms for both problems. A sensitivity
analysis is also performed to reveal different impacts of the algorithm's key
control parameters and operators on GTBO's performance.
