JuliaCon 2022 (Times are UTC)

Metaheuristics.jl: Towards Any Optimization
07-29, 16:30–17:00 (UTC), Purple

Real-world problems require sophisticated methodologies providing feasible and efficient solutions. Metaheuristics are algorithms proposed to approximate those optimal solutions in a short time, making them suitable for applications where saving time is important. Metaheuristics.jl package implements relevant state-of-the-art algorithms for constrained, multi-, many-objective and bilevel optimization. Moreover, performance indicators are implemented in this package.

This talk presents the main features of Metaheuristics.jl, which is a package for global optimization to approximate solutions for single-, multi-, and many-objective optimization. Several examples are given to illustrate the implementation and the resolution of the different optimization problems.

Jesús Mejía is a Ph.D. student from the Artificial Intelligence Research Institute at the University of Veracruz (IIIA-UV). He received a BSc degree in mathematics from the University of Veracruz and obtained a master’s degree with an honorific mention in Artificial Intelligence from CIIA-UV. His research interests include Numerical Analysis, Bilevel Optimization, and Intelligent Computing.