  A novel population-based optimization method is proposed in this paper, the
Calico Salmon Migration Algorithm (CSMA), which is inspired by the natural
behavior of calico salmon during their migration for mating. The CSMA
optimization process comprises four stages: selecting the search space by
swimming into the river, expanding the search space from the river into the
ocean, performing precise search during the migrating process, and breeding new
subspecies by the remaining calico salmon population. To evaluate the
effectiveness of the new optimizer, we conducted a series of experiments using
different optimization problems and compared the results with various
optimization algorithms in the literature. The numerical experimental results
for benchmark functions demonstrate that the proposed CSMA outperforms other
competing optimization algorithms in terms of convergence speed, accuracy, and
stability. Furthermore, the Friedman ranking test shows that the CSMA is ranked
first among similar algorithms.
