Tackle the most challenging problems in science and engineering with these cutting-edge algorithms
Multi-objective optimization problems (MOPs) are those in which more than one objective needs to be optimized simultaneously. As a ubiquitous component of research and engineering projects, these problems are notoriously challenging. In recent years, evolutionary algorithms (EAs) have shown significant promise in their ability to solve MOPs, but challenges remain at the level of large-scale multi-objective optimization problems (LSMOPs), where the number of variables increases and the optimized solution is correspondingly harder to reach.
Evolutionary Large-Scale Multi-Objective Optimization and Applications
constitutes a systematic overview of EAs and their capacity to tackle LSMOPs. It offers an introduction to both the problem class and the algorithms before delving into some of the cutting-edge algorithms which have been specifically adapted to solving LSMOPs. Deeply engaged with specific applications and alert to the latest developments in the field, it’s a must-read for students and researchers facing these famously complex but crucial optimization problems.
The book’s readers will also find:
Analysis of multi-optimization problems in fields such as machine learning, network science, vehicle routing, and more
Discussion of benchmark problems and performance indicators for LSMOPs
Presentation of a new taxonomy of algorithms in the field
is ideal for advanced students, researchers, and scientists and engineers facing complex optimization problems.