Muskrat Optimization Algorithm (MOA): A Nature-Inspired Metaheuristic for Complex Optimization Problems.

This paper proposes a novel metaheuristic optimization method, named the Muskrat Optimization Algorithm (MOA), for solving complex optimization problems. Inspired by the foraging and nesting behaviors of muskrats, MOA simulates their cooperative characteristics and spatial memory mechanism. The population is modeled as muskrats with three distinct search paths, and the optimization process is divided into two main phases: foraging and nesting. In the foraging phase, an opportunistic strategy is applied in the early stage to enhance global exploration, followed by a defensive strategy to improve convergence stability. In the nesting phase, a tentative nesting strategy is first adopted to maintain population diversity, and then a refined nesting strategy is employed to strengthen local exploitation. A dynamic memory updating mechanism further balances exploration and exploitation by adjusting historical search information. The performance of MOA is evaluated on the CEC2017 benchmark suite and three constrained engineering design problems. Experimental results demonstrate that MOA achieves competitive convergence accuracy, robustness, and overall optimization performance.