Ascaris Lumbricoides Optimization Algorithm

Swarm intelligence optimization algorithms, as important tools for solving complex optimization problems, have been extensively studied in recent years. This paper proposes a novel swarm intelligence optimization algorithm—the Ascaris Lumbricoides Optimization Algorithm (ALOA). Inspired by the peristaltic behavior and energy regulation mechanism of Ascaris lumbricoides, this algorithm constructs mathematical models including adaptive peristaltic amplitude, energy-driven cycle, spiral propagation, swarm interference, multi-strategy fusion, and energy feedback, achieving a dynamic balance between global search and local exploitation. Through rigorous mathematical modeling, this paper systematically analyzes the algorithm from the aspects of algorithm principle, update mechanism, convergence, and complexity, providing new methods and perspectives for the theoretical research and application of optimization algorithms.