In daily life and in scientific research, the problem of optimization is pervasive. Optimization refers to the process of finding optimal solutions or programmes under certain constraints. This concept has different applications in different areas, such as travel plans, fitness programmes, structure design optimization, resource allocation optimization and transport programme optimization. In order to solve these optimization problems, technological excellence continues to develop. Optimizing technologies cover operational preparation theory and computer technology, including mathematical modelling, binding processing, algorithm design and programme design. Optimization of technology is divided into traditional and modern technologies. Traditional optimized technologies focus on continuity, small-scale issues such as linear and dynamic planning. Modern optimisation techniques are designed primarily for discrete, large-scale problems, using inspirational algorithms such as genetic algorithms, simulated fire retreats, taboo searches, particle cluster algorithms, etc. These techniques can find the closest solution to the best solution without knowing the best mathematical characteristics and rely on computers to solve complex problems quickly. This course, optimization technology, will explore modern optimization techniques in depth, focusing on various intelligent optimization algorithms. These algorithms include genetic algorithms, non-dominant sequence genetic algorithms, simulated refrigeration algorithms, taboo searches, particle cluster algorithms and enhanced learning. Each algorithm will be detailed in terms of theoretical foundations, algorithmic processes, engineering and programming practices. This course combines theoretical learning with programming practices to enable students to understand and apply intelligent algorithms. Specific features include: (1) theoretical learning, concise: a clear description of the theoretical basis of the various optimized techniques and algorithms. (2) case pull, same-source profiling: to guide students to understand algorithm applications through actual cases. (3) programming practice, integration: through the python programming practice, to help students master algorithms. (4) easy to learn, results orientation: focus on students ' practical application skills to ensure that course knowledge points are easily understood. Through this course, students will be equipped with the basic theoretical and practical methods of modern technology optimization and the ability to solve the problems of practical and complex optimization。
