On global optimization

TL;DR: An emerging area of mixed-integer optimal control that adds systems of ordinary differential equations to MINLP is described and a range of approaches for tackling this challenging class of problems are discussed, including piecewise linear approximations, generic strategies for obtaining convex relaxations for non-convex functions, spatial branch-and-bound methods, and a small sample of techniques that exploit particular types of non- Convex structures to obtain improved convex Relaxations.

TL;DR: It is demonstrated that the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA.

TL;DR: This article introduces several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel, and manages to drastically reduce the computational cost of these strategies through the use of closed form formulas.

TL;DR: An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one, and continuous variables is presented in this paper, where penalties are imposed on the constraints for integer/discrete violations.

TL;DR: In this paper, the authors present a survey of applications of evolutionary algorithms and associated strategies in engineering, focusing on different areas in different fields of engineering, such as software engineering, software design, and software engineering.

TL;DR: The proposed technique does not require fine tuning of factors like the traditional penalty function and uses a self-adaptation mechanism that avoids the traditional empirical adjustment of the main genetic operators (i.e., crossover and mutation).

TL;DR: Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer.