Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2023 313654 Heuristic Algorithm 1 3.0 3 ★ Hong,Tzu-Yen GIEM
CMGT 16 0 工管所和管理博合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2023	313654	Heuristic Algorithm	1	3.0	3	★	Hong,Tzu-Yen	GIEM CMGT	16	0	工管所和管理博合開

Course Plan：

Instructor Hong,Tzu-Yen　Office Hours

Email georgehong618@gmail.com

Last Updated 2022-12-29 10:41:24

Course Objective 本課程聚焦於進化計算方法、模擬退火法、遺傳演算法、蟻群優化法和粒子群優化等啟發式演算法之介紹，以及演算法於經典最佳化問題之應用，並以管理科學之實務案例討論演算法之應用。 This course focuses on the introduction to heuristic and metaheuristic algorithms, including Evolutionary Computation, Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), and their application in well-known optimization problems. Case studies for a variety of problems will also be discussed.

Course Schedule 1 Introduction 2 Complexity and Heuristic algorithms 3 Random Walk and hill climbing 4 Random Walk and hill climbing(Coding) 5 Simulated Annealing 6 Simulated Annealing (Coding lesson) 7 Presentation 1 8 Tabu Search 9 Tabu Search(Coding lesson) 10 Presentation 2 11 Genetic Algorithm 12 Genetic Algorithm(Coding) 13 Presentation 3 14 Project Proposal 15 Particle Swarm Optimization 16 Particle Swarm Optimization(Coding) 17 Term Project Presentation 18 Term Project Presentation

Evaluation and grading policy Homework 40% Paper Presentation 30% Term Project 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Gendreau, M., & Potvin, J. Y. (Eds.). (2010). Handbook of metaheuristics. New York: Springer.

The access to curricular consultation Making an appointment with TA

The course corresponds to the SDGs

Does the course incorporate AI

Note
若受疫情影響無法教室上課，則上課方式改為teams遠距教學

This course requires basic Operations Research background and computer programming skills.

Instructor	Hong,Tzu-Yen　Office Hours
Email	georgehong618@gmail.com
Last Updated	2022-12-29 10:41:24
Course Objective	本課程聚焦於進化計算方法、模擬退火法、遺傳演算法、蟻群優化法和粒子群優化等啟發式演算法之介紹，以及演算法於經典最佳化問題之應用，並以管理科學之實務案例討論演算法之應用。 This course focuses on the introduction to heuristic and metaheuristic algorithms, including Evolutionary Computation, Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), and their application in well-known optimization problems. Case studies for a variety of problems will also be discussed.
Course Schedule	1 Introduction 2 Complexity and Heuristic algorithms 3 Random Walk and hill climbing 4 Random Walk and hill climbing(Coding) 5 Simulated Annealing 6 Simulated Annealing (Coding lesson) 7 Presentation 1 8 Tabu Search 9 Tabu Search(Coding lesson) 10 Presentation 2 11 Genetic Algorithm 12 Genetic Algorithm(Coding) 13 Presentation 3 14 Project Proposal 15 Particle Swarm Optimization 16 Particle Swarm Optimization(Coding) 17 Term Project Presentation 18 Term Project Presentation
Evaluation and grading policy	Homework 40% Paper Presentation 30% Term Project 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Gendreau, M., & Potvin, J. Y. (Eds.). (2010). Handbook of metaheuristics. New York: Springer.
The access to curricular consultation	Making an appointment with TA
The course corresponds to the SDGs
Does the course incorporate AI
Note	若受疫情影響無法教室上課，則上課方式改為teams遠距教學 This course requires basic Operations Research background and computer programming skills.