Repository of the course Nature Inspired Optimization from the Electrical Engineering Graduate Program at Federal University of Pará.
The goal of this course is to stimulate learning of concepts associated with optimization, specifically for large-scale problems, with a focus on developing modeling and solution skills through classical and nature-inspired strategies for continuous or integer variables, linear and nonlinear functions, single and multi-objective problems with or without restrictions.
The theoretical content and practical demonstrations will be taught through synchronous face-to-face meetings. As support, students will also be directed to videos and short texts selected from a careful selection of different sources.
Your frequency score will be calculated based on attendance at lectures and completion of activities.
Each student's assessment will be based on a holistic assessment across several activities, including: labs, tests, selected exercises and a final paper.
- Assingments (50%): activities, practical exercises and others.
- Final Paper (50%): a paper to be developed.
- FOGEL, D. B.; MICHALEWICZ, Z. How to Solve It: Modern Heuristics. 2nd Edition. 2013.
- SARKER, Ruhul Amin; NEWTON, Charles S. Optimization modelling: a practical approach. CRC press, 2007.
- YANG, Xin-She. Nature-inspired optimization algorithms. Academic Press, 2020.
- YANG, Xin-She. Optimization techniques and applications with examples. John Wiley & Sons, 2018.
- YANG, Xin-She. Nature-inspired metaheuristic algorithms. Luniver press, 2010.
- VAZIRANI, Vijay V. Approximation algorithms. Vol. 1. Berlin: springer, 2001.
- JUNGNICKEL, Dieter. Graphs, networks and algorithms. Berlin: Springer, 2005.
- FLOREANO, Dario; MATTIUSSI, Claudio. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press, 2023.
| N | Topic |
|---|---|
| 0 | Syllabus (Short description) / More in SIGAA) |
| 1 | Introduction to Problem Optimization and Modeling |
| 1.1 | Introduction to Modeling |
| 1.2 | Complexity Theory |
| 1.3 | Introduction to Optimization |
| 2 | Mono-Objective Optimization Modeling |
| 2.1 | Linear Programming |
| 2.2 | Non-linear Programming |
| 2.3 | Integer Linear Programming |
| 2.4 | Optimization Problems on Graphs |
| 3 | Classical Strategies for Optimization |
| 3.1 | Greedy Search, Exhaustive search, Dynamic Programming |
| 4 | Nature-inspired Solution Strategies |
| 4.1 | Introduction to Metaheuristics |
| 4.2 | Simulated Annealing |
| 4.3 | Particle Swarm Colab |
| 4.4 | Flower Polination |
| 5 | Multiobjective Optimization |
| 5.1 | Concepts and monoobjective approaching |
| 5.2 | NSGA-II |
| ID | Topic |
|---|---|
| A01 | Prep Test |
| A02 | Linear Programming Exercise |
| A03 | Integer Programming Exercise |
| A04 | Paper Reading |
| A05 | Levy Flights |
| A06 | Simulated Annealing |
- About BrownianMotion: https://scipy-cookbook.readthedocs.io/items/BrownianMotion.html
- Mathematical Programming in Python: https://www.pyomo.org/
- Nature-Inspired Methods https://github.com/Valdecy/pyMetaheuristic
- MOO Methods https://github.com/anyoptimization/pymoo