Feature Selection with a Binary Flamingo Search Algorithm and a Genetic Algorithm

Eluri, Rama Krishna; Devarakonda, Nagaraju

doi:10.1007/s11042-023-15467-x

Cited by 10 publications

(2 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Reference no. Optimization algorithm used Application Özbay 4 Modified seahorse optimization algorithm Engineering design problems Gharehchopogh and Ibrikci 6 Improved African vultures optimization algorithm Multi-level thresholding image segmentation Krishna et al 11 K-means and PSO algorithm Clustering Eluri and Devarakonda 12 Chaotic binary pelican optimization algorithm Feature selection Eluri and Devarakonda 14 Binary flamingo search algorithm and genetic algorithm Feature selection Abed-Alguni et al 15 Opposition-based sine cosine optimizer Feature selection Abed-Alguni et al 16 Improved binary djaya algorithm Feature selection Gharehchopogh et al 17 Dynamic harris hawk optimization algorithm Botnet detection in IoT Cheng and Prayogo 26 Symbiotic organisms search optimization algorithm Engineering design problems Kaur et al 27 Sandpaper optimization algorithm Engineering design problems Mohammadi-Balani et al 29 Golden eagle optimizer Engineering design problems Saremi et al 30 Grasshopper optimization algorithm Structural design problems Trojovská et al …”

Section: Basics Of Nature-inspired Algorithmsmentioning

confidence: 99%

A hybrid swarm intelligence algorithm for region-based image fusion

Salgotra,

Lamba,

Talwar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ($$Q^{AB/F}$$ Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ($$N^{AB/F}$$ N A B / F ). The average $$Q^{AB/F} = 0.765508$$ Q A B / F = 0.765508 , $$SCD = 1.63185$$ S C D = 1.63185 , $$SSIM = 0.726317$$ S S I M = 0.726317 , and $$N^{AB/F} = 0.006617$$ N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon’s and Friedman’s tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.

show abstract

Section: Basics Of Nature-inspired Algorithmsmentioning

confidence: 99%

A hybrid swarm intelligence algorithm for region-based image fusion

Salgotra,

Lamba,

Talwar

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Feature Selection with a Binary Flamingo Search Algorithm and a Genetic Algorithm is discussed by Eluri and Devarakonda 26 . They evaluate the performance of HBFS-GA using 18 different UCI datasets and various metrics.…”

Section: Introductionmentioning

confidence: 99%

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

Abdelrazek,

Abd Elaziz,

El-Baz

2024

Sci Rep

View full text Add to dashboard Cite

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

show abstract