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A star-nosed mole optimizer (SNMO): A novel optimization algorithm
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DOI：10.1016/j.compeleceng.2026.111179.png)
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Abstract
En 中文
This paper describes the new Star-Nosed Mole Optimizer (SNMO), which is a unique metaheuristic optimization algorithm that models the foraging behavior of the star-nosed mole. By incorporating both exploration and exploitation into the optimization process, SNMO provides an effective means of optimizing function performance and generating more efficient use of resources than traditional methods. The multi-sensory approach taken by SNMO in performing optimization and the dynamic transition mechanism help it quickly converge on global optimum solutions of problems in multidimensional search spaces, enabling very high-speed convergence and accurate solutions. The results of performance testing for SNMO show that it consistently performs with precision and stability regardless of the number of dimensions tested (10D, 30D, and 50D). In the 10D tests, SNMO's execution time was reduced by up to 41.09%, and the failure rate was 0% on 8 of 9 benchmarks. For 30D and 50D testing, SNMO reached theoretical global optima on 66.7% of functions tested; the standard deviation of SNMO's performance across all cases is much lower than that of Particle Swarm Optimization (PSO) and Improved PSO (IPSO) algorithms (10–5 for F7 “Ackley”), and very significant. These results indicate the superiority of SNMO in terms of both efficiency and reliability compared with other widely used optimization approaches; however, additional validation is performed for two real-world problems such as pressure vessel design and identifying optimal degradation model parameters for lithium-ion batteries. The results of the two real-world applications show the SNMO outperforms the other optimization algorithms in terms of convergence speed, solution accuracy, and failure rate.
Keywords:
Star-nosed mole
Metaheuristic optimization
Foraging behavior
Global optimum
Multi-dimensional search space
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C
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