Multi-Scenario Performance Simulation and Engineering Applications of Novel Swarm Intelligence Algorithms

Swarm Intelligence Algorithms have attracted considerable attention owing to their robust global search capabilities and wide applicability. With the continuous emergence of innovative algorithms, a significant research challenge is the comprehensive evaluation of their optimization performance alongside providing theoretical guidance for selecting and improving algorithms for practical applications. This study focuses on six newly proposed swarm intelligence algorithms introduced in 2024: Black-winged Kite Algorithm (BKA), FATA morgana Algorithm (FATA), Starfish Optimization Algorithm (SFOA), ESCape Algorithm (ESC), Differentiated Creative Search (DCS) and Snow Geese Algorithm (SGA). Through simulation-based research, we analyze their performance and applications in classical engineering design optimization problems. Key findings from our study include: (1) The DCS demonstrates superior global optimization capability in multi-modal functions and complex engineering problems, with its average optimal value significantly surpassing those of other algorithms; (2) The ESC stands out for its best-in-class robustness and adaptability, although it comes with higher computational costs; (3) BKA and FATA excel in uni-modal or real-timecritical scenarios due to their efficiency; (4) The SFOA strikes an effective balance between precision and efficiency for medium-complexity problems; (5) The SGA shows relatively weaker performance, indicating a need for further refinement to enhance its effectiveness.