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.
- Teacher: nsami nsami
