Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations
have become state of the art in algorithm design for solving real-world optimization …

Optimization and its related solving methods are becoming increasingly important in most
academic and industrial fields. The goal of the optimization process is to make a system or a …

Simulated Annealing (SA) is one of the simplest and best-known metaheuristic method for
addressing difficult black box global optimization problems whose objective function is not …

In this paper we present an extension of ant colony optimization (ACO) to continuous
domains. We show how ACO, which was initially developed to be a metaheuristic for …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

Simulated annealing is a popular local search meta-heuristic used to address discrete and,
to a lesser extent, continuous optimization problems. The key feature of simulated annealing …

We design a genetic algorithm called the orthogonal genetic algorithm with quantization for
global numerical optimization with continuous variables. Our objective is to apply methods of …

Heuristic optimization provides a robust and efficient approach for solving complex real-
world problems. The focus of this research is on a hybrid method combining two heuristic …

We present a new class of methods for the global optimization of continuous variables
based on simulated annealing (SA). The coupled SA (CSA) class is characterized by a set of …

In this paper, we present the computational tools and a hardware prototype for 3D face
recognition. Full automation is provided through the use of advanced multistage alignment …