Jellyfish, a representative class of coelenterates, possess unique biological behavioral characteristics that provide new insights into intelligent optimization algorithms. This paper proposes an optimization algorithm based on the self-organizing pulsation behavior of jellyfish (Jellyfish Self-Organizing Pulsation Optimization, JSOPO). This algorithm simulates jellyfish's pulsating propulsion, tentacle sensing, light-sensing buoyancy, and swarm self-organization. By mathematically modeling the jellyfish's behavioral mechanisms, this paper constructs a comprehensive algorithmic formula that not only reflects jellyfish biology but also can be used to solve complex optimization problems. The algorithm does not require experimental data validation; instead, it focuses on mechanism modeling and formula derivation, providing a theoretical foundation for nature-inspired intelligent optimization algorithms.