Intelligent Rainforest Anaconda Optimization Algorithm

Swarm intelligence optimization algorithms, by simulating the behavior of organisms in nature, can find optimal solutions in complex high-dimensional search spaces. This paper proposes a novel metaheuristic algorithm—the Anaconda Intelligent Optimization Algorithm (IRAOA)—inspired by the hunting and survival strategies of the giant rainforest snake (anaconda). This algorithm achieves a balance between global search and local exploitation by simulating the anaconda's ambush hunting, prey constriction, long-distance migration, environmental perception, and camouflage perturbation mechanisms. This paper provides a detailed discussion of the algorithm modeling, iterative process, mathematical formulas, characteristic analysis, application extensions, and theoretical advantages, offering a new approach to solving complex high-dimensional optimization problems. IRAOA possesses significant characteristics such as multi-strategy fusion capability, adaptive step size and energy mechanism, and camouflage perturbation to enhance resistance to local optima. It can also be flexibly extended to multi-objective optimization and hybrid intelligent algorithms.