In the past two decades, metal–organic frameworks (MOFs) or porous coordination polymers
(PCPs) assembled from metal ions or clusters and organic linkers via metal–ligand …

Many optimization problems in engineering and science require solutions that are globally
optimal. These optimization problems are characterized by the nonconvexity of the feasible …

A unified view of metaheuristics This book provides a complete background on
metaheuristics and shows readers how to design and implement efficient algorithms to solve …

The topic of this book is multistage stochastic optimization. Multistage reflects the fact that an
optimal decision is an entire strategy or policy, which is executed during subsequent instants …

This paper presents an overview of our most recent results concerning the Particle Swarm
Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting …

A frequent challenge faced by beginners in machine learning is the extensive background
required in linear algebra and optimization. One problem is that the existing linear algebra …

Polynomial extremal problems (PEP) constitute one of the most important subclasses of
nonlinear programming models. Their distinctive feature is that an objective function and …

Dimensionality reduction, also known as manifold learning, is an area of machine learning
used for extracting informative features from data for better representation of data or …

In this correspondence, an approach based on coevolutionary particle swarm optimization to
solve constrained optimization problems formulated as min-max problems is presented. In …

Optimization is a very broad field of research with a wide spectrum of important applications.
Until the 1950s, optimization was understood as a single-objective optimization, ie, as the …