An enhanced artemisinin optimization algorithm based on three-level synergistic Gaussian mutation and adaptive orthogonal learning

In the field of complex optimization problem solving, the Artemisinin Optimization (AO) simulates the multi-stage synergistic mechanism of artemisinin in the treatment of malaria. It realizes a multi-stage collaborative search through adaptive probability transformation and dynamic weight adjustment and balances global exploration and local exploitation. However, its global exploration adopts fixed probability search, which leads to insufficient exploitation and slow convergence. Static mutation parameters will cause invalid disturbance and destroy the potential solution, so there are still defects such as low convergence accuracy and sub-optimal when solving complex multimodal problems. Therefore, this paper proposes an improved AO, termed GOLAO, which integrates Three-level Synergistic Gaussian Mutation (TSGM) and Adaptive Orthogonal Learning (AOL). TSGM dynamically optimizes the search agent through the three-level coordination mechanism of’state-space-probability’, improves the diversity of the population, and significantly reduces the risk of search agents falling into sub-optimal. AOL reduces redundant computation through systematic exploration, enhances its spatial coverage ability, and improves convergence speed and precision. The performance of the GOLAO algorithm was evaluated using the benchmark function test sets from IEEE CEC2014 and IEEE CEC2022, with comparisons to other peer algorithms. The findings indicate that GOLAO demonstrates strong performance. Furthermore, to assess the practical applicability of the GOLAO algorithm in real-world optimization challenges, it is employed for breast cancer pathology image segmentation. Comparative analyses with alternative approaches are conducted across six distinct threshold levels. The results show that GOLAO has apparent advantages in the image segmentation effect.