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

18 Bat Algorithm

Summary

This chapter describes the bat algorithm (BA) that is a relatively new meta‐heuristic optimization algorithm. The basic concepts of the BA are inspired by the echolocation behavior of bats. The following sections present a literature review of the BA and its applications, a description of the analogy between the behavior of microbats and the BA, and a detailed explanation of the BA and introduce a pseudocode of the BA.

18.1 Introduction

Yang (2010) developed the bat algorithm (BA) based on the echolocation features of microbats. The continuous optimization of engineering design optimization has been extensively studied with the BA, which demonstrated that the BA can deal with highly nonlinear problems efficiently and can find the optimal solutions accurately (Yang, 2010, 2012; Yang and Gandomi, 2012). Case studies include pressure vessel design, automobile design, spring and beam design, truss systems, tower and tall building design, and others. Assessments of the BA features are found in Koffka and Ashok (2012), who compared the BA with the genetic algorithm (GA) and particle swarm optimization (PSO) in cancer research problems and provided evidence that the BA performs better than the other two algorithms. Malakooti et al. (2012) implemented the BA to solve two types of multiprocessor scheduling problems (MSP) and concluded that bat intelligence outperformed the list algorithm and the GA in the case of single‐objective MSP. Reddy and Manoj (2012) ...

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