Starfish Optimization, Sandcat Swarm Optimization, Weighted Average and Mirage Search Optimization Algorithms for Finding Global Optimal Solutions

Abstract

In this study, the performance of four contemporary meta-heuristic techniques—namely Starfish Optimization, Sandcat Swarm Optimization, Weighted Average Algorithm (WAA), and Mirage Search Optimization—is investigated across diverse optimization challenges. The central goal is to execute a rigorous, unbiased comparative analysis to identify the most proficient optimizer among them. To ensure a fair benchmark, each method was configured with identical population sizes and iteration limits across five distinct problems. Furthermore, to guarantee statistical reliability, 50 independent trials were conducted for every algorithm. Experimental data reveals that WAA outperforms its counterparts by a substantial margin, demonstrating superior stability and faster convergence speeds during the search phase. Consequently, WAA is identified as the most effective solution and is highly endorsed for addressing the optimization tasks explored in this work.