Course Title: Heuristics & Metaheuristics
Overview:
This course introduces heuristic and metaheuristic approaches for solving complex optimization problems where exact methods are inefficient or infeasible. It covers fundamental concepts, common algorithms, and practical applications in real-world domains such as logistics, scheduling, and resource allocation.
Objectives:
- Understand the principles behind heuristics and metaheuristics
- Learn how to design and implement efficient solution strategies
- Analyze and compare algorithm performance
- Apply these methods to real-world optimization problems
Skills Acquired:
- Problem modeling and abstraction
- Design of heuristic and metaheuristic algorithms (e.g., genetic algorithms, simulated annealing, tabu search)
- Performance evaluation and parameter tuning
- Critical thinking for selecting appropriate optimization methods
Prerequisites:
- Basic programming knowledge (e.g., C++, Python, or Java)
- Fundamentals of algorithms and data structures
- Basic understanding of mathematics (especially discrete math and probability)
Motivation:
Many real-world problems are too complex for exact optimization methods due to time and computational constraints. Heuristics and metaheuristics provide practical, near-optimal solutions within reasonable timeframes, making them essential tools in fields such as engineering, data science, logistics, and artificial intelligence.
- Enseignant: nsami nsami
