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Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
book

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
October 2017
Intermediate to advanced content levelIntermediate to advanced
304 pages
8h 3m
English
Wiley
Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

5 Simulated Annealing

Summary

This chapter reviews the simulated annealing (SA) algorithm. The SA is inspired by the process of annealing in metallurgy. It is one of the meta‐heuristic optimization algorithms. This chapter presents a literature review of the development and applications of the SA, followed by a description of the process of physical annealing and its mapping to the SA, which outlines the steps of the algorithm in detail. The chapter closes with a pseudocode of the SA algorithm.

5.1 Introduction

A popular algorithm in heuristic optimization, simulated annealing (SA) optimization was developed by Kirkpatrick et al. (1983), who showed how a model for simulating the annealing of solids, as proposed by Metropolis et al. (1953), could be used for solving optimization problems in which the fitness or objective function to be minimized corresponds to the energy states of the solid. Dolan et al. (1989) demonstrated the capacity of the SA for optimizing chemical processes by applying it to the design of pressure relief header networks and heat exchanger networks. Dougherty and Marryott (1991) described the SA algorithm and applied it to the optimization of groundwater management problems in combinatorial form. Wang and Zheng (1998) linked the SA with MODFLOW, a groundwater flow simulation code, for optimal management of groundwater resources. The results of the SA were compared with those obtained with linear programming, nonlinear programming, and differential ...

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Publisher Resources

ISBN: 9781119386995Purchase book