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On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 1, p e0242612 (2021)
- Publication Year :
- 2020
-
Abstract
- Butterfly Optimization Algorithm (BOA) is a recent metaheuristics algorithm that mimics the behavior of butterflies in mating and foraging. In this paper, three improved versions of BOA have been developed to prevent the original algorithm from getting trapped in local optima and have a good balance between exploration and exploitation abilities. In the first version, Opposition-Based Strategy has been embedded in BOA while in the second Chaotic Local Search has been embedded. Both strategies: Opposition-based & Chaotic Local Search have been integrated to get the most optimal/near-optimal results. The proposed versions are compared against original Butterfly Optimization Algorithm (BOA), Grey Wolf Optimizer (GWO), Moth-flame Optimization (MFO), Particle warm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA) using CEC 2014 benchmark functions and 4 different real-world engineering problems namely: welded beam engineering design, tension/compression spring, pressure vessel design, and Speed reducer design problem. Furthermore, the proposed approches have been applied to feature selection problem using 5 UCI datasets. The results show the superiority of the third version (CLSOBBOA) in achieving the best results in terms of speed and accuracy.
- Subjects :
- 0209 industrial biotechnology
Computer science
Social Sciences
02 engineering and technology
020901 industrial engineering & automation
Local optimum
0202 electrical engineering, electronic engineering, information engineering
Heuristics
Psychology
Foraging
Mammals
Multidisciplinary
Animal Behavior
Applied Mathematics
Simulation and Modeling
Eukaryota
Insects
Databases as Topic
Moths and Butterflies
Physical Sciences
Vertebrates
Benchmark (computing)
Medicine
Engineering and Technology
020201 artificial intelligence & image processing
Sensory Perception
Performance improvement
Butterflies
Algorithms
Research Article
Optimization
Mathematical optimization
Arthropoda
Science
Feature selection
Research and Analysis Methods
Animals
Metaheuristic
Global optimization
Behavior
Wolves
Organisms
Cognitive Psychology
Biology and Life Sciences
Probability Theory
Probability Distribution
Invertebrates
Amniotes
Cognitive Science
Perception
Zoology
Entomology
Mathematics
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....4da564f2ce3b854e70adaa6dfdb15a7b