A Tri-Direction Guided Moss Growth Optimizer with Adaptive Differential Evolution for Engineering Design Problems

This article proposes an improved Moss Growth Optimization (IMGO) algorithm to address the drawbacks of imbalanced exploration and exploitation and susceptibility to local optima in the original MGO. IMGO integrates three-dimensional guidance, elite guidance, adaptive search, and adaptive adversarial learning to achieve a dynamic balance between global exploration and local development. IMGO is compared with eight metaheuristic algorithms on CEC2017 and CEC2022 benchmarks. Results show IMGO ranks first in 30D and 50D CEC2017 with scores of 49 and 64, surpassing second-ranked CFDA (87 and 81). For 20D CEC2022, IMGO takes first place with a score of 27, while original MGO scores 66 and ranks seventh. The Friedman test validates its strong robustness with a statistic of 1.6897, lower than CFDA's 3.0000. Furthermore, IMGO acquires the optimal average solutions for all six engineering design problems. Experiments verify that IMGO has comprehensive advantages in accuracy and robustness, providing a reliable method for complex optimization problems.