Calico Salmon Migration Algorithm: A novel meta-heuristic optimization algorithm

TL;DR

The Calico Salmon Migration Algorithm (CSMA) is a new population-based optimization method inspired by salmon migration, demonstrating superior performance over existing algorithms in convergence speed, accuracy, and stability across benchmark tests.

Contribution

This paper introduces the CSMA, a novel meta-heuristic inspired by natural salmon migration, with a four-stage process that enhances optimization performance.

Findings

CSMA outperforms existing algorithms in convergence speed.

CSMA achieves higher accuracy and stability in benchmark tests.

Friedman ranking places CSMA first among similar algorithms.

Abstract

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.