Bat algorithm for constrained optimization tasks

In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported...

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Bibliographic Details
Published in:Neural computing & applications Vol. 22; no. 6; pp. 1239 - 1255
Main Authors: Gandomi, Amir Hossein, Yang, Xin-She, Alavi, Amir Hossein, Talatahari, Siamak
Format: Journal Article
Language:English
Published: London Springer-Verlag 01.05.2013
Springer
Subjects:
Applied sciences
Artificial Intelligence
Calculus of variations and optimal control
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Learning and adaptive systems
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Original Article
Probability and Statistics in Computer Science
Sciences and techniques of general use
Constraint optimization
Metaheuristic algorithm
Bat algorithm
Engineering
Neural computation
Algorithm performance
Optimal solution
Optimization method
Benchmarks
Neural network
Algorithm
Constrained optimization
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to solve constraint optimization tasks. BA is verified using several classical benchmark constraint problems. For further validation, BA is applied to three benchmark constraint engineering problems reported in the specialized literature. The performance of the bat algorithm is compared with various existing algorithms. The optimal solutions obtained by BA are found to be better than the best solutions provided by the existing methods. Finally, the unique search features used in BA are analyzed, and their implications for future research are discussed in detail.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-012-1028-9