Global optimization methods for engineering applications: A review

Abstract

A review of the methods for global optimization rea¢ veals that most methods have been developed for unconstrained ~t problems. They need to be extended to general constrained c~ problems because most of the engineering applications have conAx(e) straints. Some of the methods can be easily extended while others need further work. It is also possible to transform a constrained D problem to an unconstrained one by using penalty or augmented det(°)Lagrangian methods and solve the problem that way. Some of the global optimization methods find all the local minimum points dj while others find only a few of them. In any case, all the methods require a very large number of calculations. Therefore, the dt computational effort to obtain a global solution is generally subdx stantial. The methods for global optimization can be divided into two broad categories: deterministic and stochastic. Some deter-~(t) ministic methods are based on certain assumptions on the cost e0 function that are not easy to check. These methods are not very exp (°)

Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

“…Global optimization methods have been subject of extensive research in the past (Arora et al, 1995) and provide means of approximating non-convex optimization problems without premature convergence to local optima. Examples for these methods are simulated annealing methods and evolutionary algorithms.…”

Automated conceptual design of manufacturing workcells in radioactive environments

“…• Tabu search -This search builds a historical record of investigated feasible solutions and uses it to investigate new solutions and escape local minima [Arora, 1995].…”

Distributed database and knowledge base modeling for concurrent design

“…In the past decade, some stochastic approaches such as Simulated Annealing, Genetic Algorithms, and Particle Swarm Optimization have been studied and effectively applied to a wide range of industry applications [Arora et al 1995, Shi et al 1997 University of Calgary was employed in the research discussed in this thesis.…”