Desert Rattlesnake Optimization Algorithm

Swarm intelligence optimization algorithms solve complex optimization problems by simulating the behavior of organisms in nature. This paper proposes a novel swarm intelligence optimization algorithm—the Desert Rattlesnake Optimization Algorithm (DSOA). This algorithm achieves an efficient combination of global search and local exploitation by simulating the thermal localization, ambush hunting, and energy management behaviors of the desert rattlesnake. The algorithm design incorporates innovative strategies such as a two-layer thermal sensing mechanism, an adaptive ambush-surprise strategy, a dynamic risk-reward trade-off, individual memory and neighbor learning mechanisms, and chaotic perturbation and adaptive decay weights. Mathematical modeling details the algorithm's search behavior, energy update, and step size control mechanism, providing an efficient, stable, and robust solution method for complex optimization problems. This paper provides a systematic mathematical theoretical foundation for DSOA and lays the groundwork for its subsequent applications and improvements.