Abstract

Heuristic optimization algorithms have significant advantages in solving complex optimization problems, and are particularly suitable for scenarios with complex objective functions, large search spaces, and numerous constraints. This paper proposes an optimization algorithm based on the professional behavior of biology teachers, called the Biology Teacher Optimization Algorithm (BTOA). The algorithm extracts heuristic rules from biology teachers' behaviors such as observation, teaching, motivation, and classroom management to form a unique search mechanism. This paper focuses on the four core mechanisms of the algorithm: an experimental observation feedback mechanism, a class cooperative learning mechanism, a knowledge accumulation and memory mechanism, and a classroom pressure adaptation mechanism, and provides detailed mathematical formulas to illustrate the algorithm process. This algorithm does not rely on experimental verification, but demonstrates its balanced strategy of global exploration and local development through mathematical modeling, reflecting its theoretical innovation and application potential.