Covering hybrid metaheuristics, parallel and distributed metaheuristics, and single–solution metaheursistics, Metaheuristics on Graphics Processing Units deftly explains how to design effective metaheuristics on GPUs for optimization and machine learning problems. Real–world examples and case studies, along with problems and solutions, demonstrate how metaheuristics are applied to such fields as telecommunication, logistics, and transportation. The veteran authors provide a complete background of metaheuristics and GPU that enables undergraduate and postgraduate students as well as engineers to design and deploy powerful algorithms to solve complex optimization problems in a diverse range of applications.