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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

12 Honey‐Bee Mating Optimization

Summary

This chapter describes the honey‐bee mating optimization (HBMO) algorithm, which is based on the mating strategy of honey bees. The chapter presents a review of the HBMO, its applications, fundamentals, algorithmic steps, and a pseudocode.

12.1 Introduction

Honey bees are social insects that live in large and well‐organized hives. Social intelligence, observance of collective rules, and division of labor are some of the traits that honey bees exhibit. Honey bees mate and reproduce in a unique way. The honey‐bee mating optimization (HBMO) algorithm is inspired by the honey‐bee mating process. It was developed and applied to reservoir operation by Bozorg‐Haddad et al. (2006). Bozorg‐Haddad and Mariño (2007) proposed dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with the HBMO algorithm. Bozorg‐Haddad et al. (2009) applied the HBMO to solve non‐convex optimization problems. Several studies have reported the successful application of the HBMO algorithm to solve a variety of problems such as water reservoir operation (Afshar et al., 2007; Bozorg‐Haddad and Mariño, 2008; Bozorg‐Haddad et al., 2008b, 2010a, b; Afshar et al., 2011), water distribution networks (Bozorg‐Haddad et al., 2008a; Jahanshahi and Bozorg‐Haddad, 2008; Ghajarnia et al., 2009, 2011; Soltanjalili et al., 2011; Sabbaghpour et al., 2012; Soltanjalili et al., 2013a, b; Solgi et al., 2015; Bozorg‐Haddad et al., 2016a, ...

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Publisher Resources

ISBN: 9781119386995Purchase book