Silverfish Optimization Algorithm

Swarm intelligence optimization algorithms solve complex optimization problems by simulating the collective behavior of organisms in nature. This paper proposes a novel swarm intelligence algorithm—the Silverfish Optimization Algorithm (SFOA)—inspired by the rapid swimming, environmental perception, and cooperative behavior of silverfish in humid environments. The algorithm achieves an efficient balance between global search and local development by introducing global random exploration, neighbor cooperation, local fine-tuning, environmental perception mechanisms, and swarm memory mechanisms. This paper details the algorithm modeling, mathematical formula description, iterative process, characteristic analysis, and application potential, and theoretically analyzes the algorithm's convergence and swarm intelligence characteristics. Results show that SFOA possesses strong global search capabilities, fine-tuning local optimization capabilities, dynamic adaptive characteristics, and good swarm cooperation effects, making it suitable for various continuous and combinatorial optimization problems.