A Novel Magnificent Frigatebird Optimization Algorithm with Proposed Movement Strategies for Enhanced Global Search

Global optimization is a fundamental tool for addressing complex and nonlinear problems across scientific and technological domains. The primary objective of this work is to enhance the efficiency, stability, and convergence speed of the Magnificent Frigatebird Optimization (MFO) algorithm by introducing new strategies that strengthen both global exploration and local exploitation. To this end, we propose an improved version of MFO that incorporates three novel movement strategies (aggressive, conservative, and mixed), a BFGS-based local search procedure for more accurate solution refinement, and a dynamic termination criterion capable of detecting stagnation and reducing unnecessary function evaluations. The algorithm is extensively evaluated on a diverse set of benchmark functions, demonstrating substantially lower computational cost and higher reliability compared to classical evolutionary and swarm-based methods. The results confirm the effectiveness of the proposed modifications and highlight the potential of the enhanced MFO for application to demanding real-world optimization problems.