Optimization algorithms have long been a core research topic in artificial intelligence and computational intelligence. In recent years, heuristic algorithms have garnered widespread attention for their powerful global search capabilities and adaptability to complex problems. This paper proposes an optimization algorithm inspired by dancer behavior, called Dancer Actor Optimization (DAO). This algorithm leverages the three core characteristics of dancers: improvisation, emotional resonance, and combinatorial optimization of movement sequences. It then establishes a mathematical model for individual movement updating, group collaboration, and sequence gradient fine-tuning. By analyzing the algorithmic mechanism and formula derivation, this paper demonstrates the algorithm's innovativeness and theoretical feasibility, providing a novel heuristic solution to complex optimization problems. While experimental verification is not performed, this paper focuses on algorithm design and mathematical analysis.