In this introductory chapter, we present a high-level description of optimization, blackbox
optimization, and derivative-free optimization. We introduce some basic optimization …

Comparing, or benchmarking, of optimization algorithms is a complicated task that involves
many subtle considerations to yield a fair and unbiased evaluation. In this paper, we …

The design and development of metaheuristic algorithms can be time-consuming and
difficult for a number of reasons including the complexity of the problems being tackled, the …

Metaheuristic and heuristic methods have many tunable parameters, and choosing their
values can increase their ability to deal with hard optimization problems. Automated …

In this paper we are concerned with multiscale modeling, control, and simulation of self-
organizing agents leaving an unknown area under limited visibility, with special emphasis …

This problem involves optimizing product collection and redistribution from production
locations to a set of processing plants over a planning horizon. This horizon consists of …

Black-box optimization (BBO) is a rapidly growing field of optimization and a topic of critical
importance in many areas including complex systems engineering, energy and the …

The performance of deep neural networks is highly sensitive to the choice of the
hyperparameters that define the structure of the network and the learning process. When …

A direct-search derivative-free Matlab optimizer for bound-constrained problems is
described, whose remarkable features are its ability to handle a mix of continuous and …

In this survey we consider mathematical models and methods recently developed to control
crowd dynamics, with particular emphasis on egressing pedestrians. We focus on two …