What are the traditional optimization algorithms and modern optimization algorithms? What is the difference?

1. Traditional optimization algorithms are generally aimed at structural problems, and have a clear description of problems and conditions, such as linear programming, quadratic programming, integer programming, mixed programming, constrained and unconstrained. , that is, clear structural information; However, intelligent optimization algorithms generally describe more general problems and generally lack structured information.

2. Many traditional optimization algorithms belong to the category of convex optimization and have unique and clear global optimization; However, most intelligent optimization algorithms are aimed at multi-extreme problems. How to prevent falling into local optimum and find global optimum as much as possible is the fundamental reason for adopting intelligent optimization algorithm: for unipolar problems, traditional algorithms are good enough most of the time, while intelligent algorithms have no advantage; For multi-extreme problems, intelligent optimization algorithm can find a good balance between jumping out of local optimum and converging to a point through its effective design, so as to find the global optimum, but sometimes the local optimum is acceptable, so the traditional algorithm also has great application space and improvement possibility for special structures.

3. Traditional optimization algorithms are generally deterministic, with fixed structure and parameters, and the computational complexity and convergence can be analyzed theoretically; Most intelligent optimization algorithms are heuristic algorithms, which can be qualitatively analyzed but difficult to quantitatively prove. Moreover, most algorithms are based on random characteristics, and their convergence is generally probabilistic. The actual performance is uncontrollable, and the convergence speed is often slow and the calculation complexity is high.