Multiobjective Optimization using Metaheuristics

DATES: 14-18 March 2022 (5 sessions)
TIME: 9:30 - 11:30 (a total of 10 hours)
LOCATION: BCAM and Online

ABSTRACT:
The course will cover the fundamental concepts related to multi-objective optimization, as well as different techniques to solve these problems and also a number of standard techniques that are used throughout to assess performance of multi-objective metaheuristics. Although the course will emphasize the research work done on multi-objective evolutionary algorithms, it will also put light on other bio-inspired metaheuristics (e.g., particle swarm optimizers, tabu search, artificial immune systems, and differential evolution, among others).

CONTENT
- Basic Concepts
- A review of multi-objective evolutionary algorithms
- Techniques to maintain diversity
- Test problems
- Performance indicators
- Hybrid approaches
- Other bio-inspired metaheuristics


PREREQUISITES:
Some basic knowledge about evolutionary algorithms and statistics is desirable, but not mandatory.

The course is addressed to Senior undergraduate students and graduate students (Masters or PhD) in Computer Science and related areas (e.g., Operations Research, Applied Mathematics).


REFERENCES:
Carlos A. Coello Coello, Gary B. Lamont and David A. Van Veldhuizen, Evolutionary Algorithms for Solving Multiobjective Problems, Second Edition, Springer, New York, ISBN 978-0-387-33254-3, September 2007.

We will also use several papers from the EMOO repository: http://emoo.cs.cinvestav.mx/


*Registration is free, but mandatory before 10 March. To sign-up go to https://forms.gle/4Emhmyxu3FaByRw68 and fill the registration form.

• BCAM-Course_20220314