# Improved Superb Fairy-Wren Optimization Algorithm and Its Application

## TL;DR

This paper introduces an improved version of the Superb Fairy-wren Optimization Algorithm to better solve complex optimization problems.

## Contribution

The novel contribution is the ISFOA algorithm, enhanced with four strategies to improve convergence and avoid local optima.

## Key findings

- ISFOA outperforms original SFOA and other algorithms in convergence accuracy and robustness.
- The four strategies in ISFOA effectively balance global exploration and local exploitation.
- ISFOA shows practical applicability in solving engineering design problems.

## Abstract

The Superb Fairy-wren Optimization Algorithm (SFOA) is a meta-heuristic algorithm inspired by the behavior of the superb fairy-wren. However, the conventional SFOA tends to converge to local optima and exhibits limited convergence accuracy when addressing complex optimization problems. To overcome these drawbacks, this study proposes an Improved Superb Fairy-wren Optimization Algorithm (ISFOA). The ISFOA incorporates four strategies—Chebyshev chaotic mapping, an adaptive weighting factor, Cauchy–Gaussian mutation, and t-distribution perturbation—to enhance the algorithm’s ability to balance global exploration and local exploitation. An ablation study using the CEC 2021 test suite was performed to evaluate the individual contribution of each strategy. Moreover, to comprehensively assess the performance of ISFOA, a comparative analysis was carried out against eight other meta-heuristic algorithms on both the CEC2005 and CEC2021 benchmark function sets. Additionally, the practical applicability of ISFOA was examined by comparing it with eight other optimization algorithms across seven engineering design problems. The comprehensive experimental results indicate that ISFOA outperforms the original SFOA and other compared algorithms in terms of robustness and convergence accuracy, thereby offering an efficient and reliable approach for solving complex optimization problems.