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