A Modified Dragonfly Optimization Algorithm for Single- and Multiobjective Problems Using Brownian Motion

The dragonfly algorithm (DA) is one of the optimization techniques developed in recent years. The random flying behavior of dragonflies in nature is modeled in the DA using the Levy flight mechanism (LFM). However, LFM has disadvantages such as the overflowing of the search area and interruption of...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2019-01, Vol.2019 (2019), p.1-17
Main Authors: Aci, Cigdem Inan, Gulcan, Hakan
Format: Article
Language:English
Subjects:
Algorithms
Animals
Benchmarking
Brownian motion
Civil engineering
Comparative analysis
Computer Simulation - economics
Design modifications
Mathematical optimization
Models, Biological
Motion
Multiple objective analysis
Odonata
Optimization
Optimization algorithms
Optimization techniques
Overflow
Parameter estimation
Problem Solving
R&D
Research & development
Studies
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Summary:The dragonfly algorithm (DA) is one of the optimization techniques developed in recent years. The random flying behavior of dragonflies in nature is modeled in the DA using the Levy flight mechanism (LFM). However, LFM has disadvantages such as the overflowing of the search area and interruption of random flights due to its big searching steps. In this study, an algorithm, known as the Brownian motion, is used to improve the randomization stage of the DA. The modified DA was applied to 15 single-objective and 6 multiobjective problems and then compared with the original algorithm. The modified DA provided up to 90% improvement compared to the original algorithm’s minimum point access. The modified algorithm was also applied to welded beam design, a well-known benchmark problem, and thus was able to calculate the optimum cost 20% lower.
ISSN:1687-5265
1687-5273
DOI:10.1155/2019/6871298