Skip to Main Content
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

7 Ant Colony Optimization

Summary

This chapter describes ant colony optimization (ACO). The basic concepts of the ACO are derived from analogy to the foraging behavior of ants. The chapter begins with a brief literature review highlighting the development and applications of the ACO. This is followed by a description of the ACO’s algorithm. A pseudocode of the ACO closes the chapter.

7.1 Introduction

Ant colony optimization (ACO) was introduced by Dorigo et al. (1991, 1996). It attempts to simulate in algorithmic fashion the foraging behavior of ants. Several varieties of ACO algorithms have appeared since its original inception, and those include the elitist ant system (AS) (Dorigo, 1992; Dorigo et al., 1996), Ant‐Q (Gambardella and Dorigo, 1995), ant colony system (Gambardella and Dorigo, 1996; Dorigo and Gambardella, 1997), max–min AS (Stutzle and Hoos, 2000), and the hypercube AS (Blum and Dorigo, 2004). The ACO has solved various types of problems such as vehicle routing (Reimann et al., 2004), project scheduling (Merkle et al., 2002), and open shop scheduling (Blum, 2005). Various types of ant‐based algorithms have found frequent implementations in civil engineering and structural optimization (Christodoulou, 2010; Lee, 2012; Sharafi et al., 2012). Abadi and Jalili (2006) applied the ACO for network vulnerability analysis. Effatnejad et al. (2013) implemented the ACO for determining the feasible optimal solution of economic dispatching. Afshar et al. (2015) wrote ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Optimization for Engineering Problems

Optimization for Engineering Problems

Kaushik Kumar, J. Paulo Davim

Publisher Resources

ISBN: 9781119386995Purchase book