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This paper proposes a novel nature-inspired meta-heuristic algorithm calledthe Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems. Itmimics golden tortoise beetle's behavior of changing colors to attract oppositesex for mating and its protective strategy that uses a kind of anal fork todeter predators. The algorithm is modeled based on the beetle's dualattractiveness and survival strategy to generate new solutions for optimizationproblems. To measure its performance, the proposed GTBO is compared with fiveother nature-inspired evolutionary algorithms on 24 well-known benchmarkfunctions investigating the trade-off between exploration and exploitation,local optima avoidance, and convergence towards the global optima isstatistically significant. We particularly applied GTBO to two well-knownengineering problems including the welded beam design problem and the geartrain design problem. The results demonstrate that the new algorithm is moreefficient than the five baseline algorithms for both problems. A sensitivityanalysis is also performed to reveal different impacts of the algorithm's keycontrol parameters and operators on GTBO's performance.

Golden Tortoise Beetle Optimizer: A Novel Nature-Inspired Meta-heuristic Algorithm for Engineering Problems