Domestic Sheep Optimization Algorithm

This paper proposes an optimization algorithm based on the behavior of domestic sheep flocks, the Domestic Sheep Optimization Algorithm (DSOA), and conducts systematic optimization design. This algorithm simulates the social behavior of domestic sheep in natural environments, including mechanisms such as information transfer, leader rotation, environmental memory, and multi-scale foraging, thereby achieving an effective balance between global search and local exploitation. The algorithm describes in detail the process of individual energy update, social vigilance signal propagation, environmental memory reference update, position update, and global optimal solution update using plain text mathematical formulas. The social vigilance mechanism enables individuals to quickly respond to local stagnation or potential optimal areas. The leader rotation strategy ensures search direction diversity. The environmental memory mechanism improves local search accuracy. The multi-scale foraging mechanism combines micro-level refined exploitation with macro-level leap exploration to achieve adaptive search. The optimized DSOA has strong scalability and theoretical feasibility, providing a new heuristic optimization method for continuous optimization problems. This work provides new ideas and methodological support for the design and application of nature-inspired optimization algorithms.