pith. sign in

arxiv: 2605.18460 · v1 · pith:RNJ77EMJnew · submitted 2026-05-18 · 💻 cs.AI · cs.LG· cs.NE

When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization

MKA Ariyaratne , Azwirman Gusrialdi , Yury Nikulin , Jaakko Peltonen This is my paper

Pith reviewed 2026-05-20 11:11 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.NE
keywords firefly algorithmautomatic clusteringmulti-objective fitnessTSP navigation penaltycentroid movementrobotic sensor networksdata clustering
0
0 comments X

The pith

A firefly algorithm variant automatically estimates the optimal number of clusters using centroid movement and a balanced multi-objective fitness function.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents a modified firefly algorithm for data clustering that avoids the need to pre-specify the number of clusters. Standard approaches like K-Means often fail when clusters have irregular shapes or different densities. The new method moves cluster centers according to a specific strategy and evaluates solutions with a fitness function that combines measures of cluster compactness and separation with a penalty drawn from traveling salesman problem solutions. This combination lets the algorithm determine cluster counts and boundaries on its own. Tests in robotic sensor networks show gains in cluster quality and shorter paths inside clusters compared with K-Means.

Core claim

The paper claims that introducing a centroid movement strategy together with a multi-objective fitness function that balances compactness, separation, and a TSP-based navigation penalty allows the firefly algorithm to automatically estimate the optimal number of clusters, adjust boundaries dynamically, and achieve better clustering quality with reduced intra-cluster path distances than K-Means on spatial tasks.

What carries the argument

Centroid movement strategy combined with a multi-objective fitness function that incorporates compactness, separation, and a TSP-based navigation penalty.

If this is right

  • The algorithm determines the number of clusters automatically without prior specification.
  • It handles non-uniform shapes and densities better than methods that assume fixed counts.
  • Intra-cluster path lengths decrease in robotic sensor network settings.
  • Cluster boundaries shift during optimization rather than staying fixed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The navigation penalty idea could transfer to other swarm methods that optimize routes or groupings in space.
  • Similar objective balancing might help automatic grouping tasks outside the current sensor-network focus.
  • Higher-dimensional versions would likely need recalibration of how the three fitness terms interact.

Load-bearing premise

The three parts of the multi-objective fitness function can be combined so that the TSP navigation penalty gives useful guidance without being overwhelmed by the other terms or requiring heavy tuning.

What would settle it

Apply the algorithm to synthetic datasets with known non-uniform clusters and compare the automatically chosen cluster count plus quality metrics against ground truth; clear failure to match or exceed K-Means would disprove the main claim.

read the original abstract

This work presents a novel variant of the Firefly Algorithm (FA) for data clustering, addressing limitations of traditional methods like K-Means that struggle with non-uniform cluster shapes, densities, and the need for pre-defining the number of clusters. The proposed algorithm introduces a centroid movement strategy and a multi-objective fitness function that balances compactness, separation, and a novel TSP-based navigation penalty. It automatically estimates the optimal number of clusters and dynamically adjusts cluster boundaries. Application to robotic sensor networks highlights its practical value, with experiments showing improved clustering quality and reduced intra-cluster path distances compared to K-Means. These results confirm the algorithm's robustness in complex spatial clustering tasks, with potential for future extensions to higher-dimensional and adaptive scenarios.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a centroid-guided variant of the Firefly Algorithm for automatic data clustering. It defines explicit centroid movement rules and a multi-objective fitness function as a weighted sum of compactness, separation, and a novel TSP-based navigation penalty term. The method claims to automatically determine the number of clusters without pre-specification and reports superior clustering quality plus reduced intra-cluster path distances relative to K-Means on robotic sensor network data.

Significance. If the comparative results hold under proper controls, the approach offers a practical heuristic for clustering irregular or density-varying data where K-Means is known to underperform, with the TSP penalty providing a domain-specific navigation signal useful in path-planning contexts such as sensor networks. The explicit update rules and weighted-sum formulation are clearly stated, though the work positions itself as an engineering contribution rather than a parameter-free theoretical advance.

major comments (2)
  1. [§4.2] §4.2 (Fitness Function): the three-term weighted sum is presented without any ablation study or weight-sensitivity analysis; it is therefore unclear whether the TSP penalty contributes independent signal or is dominated by the compactness and separation terms under the chosen weights.
  2. [§5] §5 (Experiments): the reported improvements over K-Means are given without the number of independent runs, standard deviations, or statistical significance tests, so the strength of the central empirical claim cannot be fully evaluated from the presented evidence.
minor comments (2)
  1. [Abstract] The abstract states 'improved clustering quality' but does not name the concrete metrics (e.g., intra-cluster sum of squares, silhouette score) used to quantify the improvement.
  2. [§3.3] Notation for the TSP penalty term could be clarified by explicitly relating it to the standard TSP formulation rather than leaving the navigation interpretation implicit.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment below and describe the revisions we will incorporate to strengthen the presentation and evaluation of our centroid-guided Firefly Algorithm.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (Fitness Function): the three-term weighted sum is presented without any ablation study or weight-sensitivity analysis; it is therefore unclear whether the TSP penalty contributes independent signal or is dominated by the compactness and separation terms under the chosen weights.

    Authors: We agree that an ablation study and weight-sensitivity analysis would clarify the role of the TSP navigation penalty. In the revised manuscript we will add an ablation experiment comparing the full three-term fitness against ablated versions that omit the TSP term, together with a sensitivity analysis that varies each weight by ±20% around the reported values while holding the others fixed. These results will be presented in an expanded §4.2 and will show that the TSP term supplies an independent signal for reducing intra-cluster path lengths on the sensor-network data. revision: yes

  2. Referee: [§5] §5 (Experiments): the reported improvements over K-Means are given without the number of independent runs, standard deviations, or statistical significance tests, so the strength of the central empirical claim cannot be fully evaluated from the presented evidence.

    Authors: We acknowledge that the current experimental reporting lacks the statistical detail needed for rigorous evaluation. We will re-execute the experiments using 30 independent runs per algorithm, report mean and standard deviation for all clustering metrics and path-length measures, and add paired statistical tests (Wilcoxon signed-rank or t-test with Bonferroni correction) comparing our method against K-Means. The updated tables and text will appear in the revised §5. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation or construction

full rationale

The manuscript presents a heuristic clustering algorithm extending the Firefly Algorithm with explicit centroid movement rules and a multi-objective fitness function defined as a weighted combination of compactness, separation, and a TSP-based navigation term. These elements are introduced as design choices within the method, with value demonstrated via comparative experiments on clustering quality and path distances rather than any first-principles derivation or prediction that reduces to fitted inputs by construction. No self-definitional equations, load-bearing self-citations, or ansatzes smuggled via prior work appear in the core construction; the approach is self-contained as a practical heuristic without claiming parameter-free optimality or uniqueness theorems.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard assumptions of metaheuristic search and the validity of the chosen fitness components; no new physical entities are introduced, but the balance of the multi-objective function likely involves tunable weights.

free parameters (1)
  • weights for compactness, separation, and TSP penalty terms
    Multi-objective fitness requires balancing coefficients that are typically fitted or chosen by hand to achieve the reported improvements.
axioms (1)
  • domain assumption Firefly Algorithm movement rules can be extended with centroid guidance to improve clustering convergence
    Invoked when proposing the centroid movement strategy as an enhancement.

pith-pipeline@v0.9.0 · 5670 in / 1279 out tokens · 28301 ms · 2026-05-20T11:11:25.847398+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

76 extracted references · 76 canonical work pages · 1 internal anchor

  1. [1]

    Data clustering: a review

    Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM compu ting surveys (CSUR). 1999;31(3):264-323

    work page 1999

  2. [2]

    On the Complexity of Clustering Problems

    Brucker P. On the Complexity of Clustering Problems. In: Henn R, Korte B, Oettli W, editors. Optimization and Operations Research. Berlin, Heidelber g: Springer Berlin Heidelberg; 1978. p. 45-54

    work page 1978

  3. [3]

    Algorithmic complexity: three NP-hard problems in compu tational statis- tics

    Welch WJ. Algorithmic complexity: three NP-hard problems in compu tational statis- tics. Journal of Statistical Computation and Simulation. 1982;15(1 ):17-25

    work page 1982

  4. [4]

    The k-means algorithm: A comprehe nsive survey and performance evaluation

    Ahmed M, Seraj R, Islam SMS. The k-means algorithm: A comprehe nsive survey and performance evaluation. Electronics. 2020;9(8):1295

    work page 2020

  5. [5]

    Fuzzy c-means algorithm-a review

    Suganya R, Shanthi R. Fuzzy c-means algorithm-a review. Inte rnational Journal of Scientific and Research Publications. 2012;2(11):1. 28

    work page 2012

  6. [6]

    A simple and fast algorithm for K-medoids cluster ing

    Park HS, Jun CH. A simple and fast algorithm for K-medoids cluster ing. Expert systems with applications. 2009;36(2):3336-41

    work page 2009

  7. [7]

    X-means: Extending k-means with efficien t estimation of the number of clusters

    Pelleg D, Moore A W, et al. X-means: Extending k-means with efficien t estimation of the number of clusters. In: Icml. vol. 1. Stanford, CA; 2000. p. 7 27-34

    work page 2000

  8. [8]

    Nelder-mead algorithm

    Singer S, Nelder J. Nelder-mead algorithm. Scholarpedia. 2009;4( 7):2928

    work page 2009

  9. [9]

    Convex optimization

    Boyd S, Boyd SP, Vandenberghe L. Convex optimization. Cambrid ge university press; 2004

    work page 2004

  10. [10]

    Adaptation in natural and artificial systems: a n introductory analysis with applications to biology, control, and artificial intelligence

    Holland JH, et al. Adaptation in natural and artificial systems: a n introductory analysis with applications to biology, control, and artificial intelligence. MIT pr ess; 1992

    work page 1992

  11. [11]

    Differential evolution–a simple and efficient heur istic for global opti- mization over continuous spaces

    Storn R, Price K. Differential evolution–a simple and efficient heur istic for global opti- mization over continuous spaces. Journal of global optimization. 1 997;11(4):341-59

    work page

  12. [12]

    Particle swarm optimization

    Kennedy J, Eberhart R. Particle swarm optimization. In: Proce edings of ICNN’95- international conference on neural networks. vol. 4. IEEE; 199 5. p. 1942-8

    work page 1942

  13. [13]

    Ant colony optimization: a new meta-heurist ic

    Dorigo M, Di Caro G. Ant colony optimization: a new meta-heurist ic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. N o. 99TH8406). vol. 2. IEEE; 1999. p. 1470-7

    work page 1999

  14. [14]

    Firefly algorithms for multimodal optimization

    Yang XS. Firefly algorithms for multimodal optimization. In: Inte rnational symposium on stochastic algorithms. Springer; 2009. p. 169-78

    work page 2009

  15. [15]

    Cuckoo search via L´ evy flights

    Yang XS, Deb S. Cuckoo search via L´ evy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC). Ieee; 2009. p. 210-4

    work page 2009

  16. [16]

    A tabu search approach to the clustering problem

    Al-Sultan KS. A tabu search approach to the clustering problem . Pattern recognition. 1995;28(9):1443-51

    work page 1995

  17. [17]

    Genetic algorithm-based clustering technique

    Maulik U, Bandyopadhyay S. Genetic algorithm-based clustering technique. Pattern recognition. 2000;33(9):1455-65

    work page 2000

  18. [18]

    A novel differential evolution based clustering a lgorithm for wireless sensor networks

    Kuila P, Jana PK. A novel differential evolution based clustering a lgorithm for wireless sensor networks. Applied soft computing. 2014;25:414-25

    work page 2014

  19. [19]

    A comprehensive review of the firefl y algorithms for data clustering

    Ariyaratne M, Fernando T. A comprehensive review of the firefl y algorithms for data clustering. Advances in Swarm Intelligence: Variations and Adaptat ions for Optimiza- tion Problems. 2022:217-39

    work page 2022

  20. [20]

    A modified grey wolf optimize r based data clus- tering algorithm

    Ahmadi R, Ekbatanifard G, Bayat P. A modified grey wolf optimize r based data clus- tering algorithm. Applied Artificial Intelligence. 2021;35(1):63-79. 29

    work page 2021

  21. [21]

    An application of particle swarm optimizat ion algorithm to clustering analysis

    Kuo R, Wang M, Huang T. An application of particle swarm optimizat ion algorithm to clustering analysis. Soft Computing. 2011;15:533-42

    work page 2011

  22. [22]

    A Two-Layer Con trol Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Net work

    Atman MWS, Fikri MR, Priandana K, Gusrialdi A. A Two-Layer Con trol Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Net work. IEEE Access. 2024;12:4153-65

    work page 2024

  23. [23]

    Genetic Algorithm with New Fitness Function for Cluster- ing

    Akay ¨O, Tekeli E, Y¨ uksel G. Genetic Algorithm with New Fitness Function for Cluster- ing. Iranian Journal of Science and Technology, Transactions A: S cience. 2020;44(3):865- 74

    work page 2020

  24. [24]

    Algorithm AS 136: A k-means clustering alg orithm

    Hartigan JA, Wong MA. Algorithm AS 136: A k-means clustering alg orithm. Journal of the royal statistical society series c (applied statistics). 1979 ;28(1):100-8

    work page 1979

  25. [25]

    The growth of cluster analysis: Tryon, Ward, and Johnson

    Blashfield RK. The growth of cluster analysis: Tryon, Ward, and Johnson. Multivariate behavioral research. 1980;15(4):439-58

    work page 1980

  26. [26]

    Finding roles of players in football using automatic particle swarm optimization-clustering algorithm

    Behravan I, Zahiri SH, Razavi SM, Trasarti R. Finding roles of players in football using automatic particle swarm optimization-clustering algorithm. Big data . 2019;7(1):35-56

    work page 2019

  27. [27]

    Evaluation of text document clustering app roach based on particle swarm optimization

    Karol S, Mangat V. Evaluation of text document clustering app roach based on particle swarm optimization. Open Computer Science. 2013;3(2):69-90

    work page 2013

  28. [28]

    Document clustering using part icle swarm optimiza- tion

    Cui X, Potok TE, Palathingal P. Document clustering using part icle swarm optimiza- tion. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, 200 5. SIS 2005. IEEE

    work page 2005

  29. [29]

    Particle swarm optimization m ethod for im- age clustering

    Omran M, Engelbrecht AP, Salman A. Particle swarm optimization m ethod for im- age clustering. International Journal of Pattern Recognition an d Artificial Intelligence. 2005;19(03):297-321

    work page 2005

  30. [30]

    Image clustering using particle swarm opt imization

    Wong MT, He X, Yeh WC. Image clustering using particle swarm opt imization. In: 2011 IEEE Congress of Evolutionary Computation (CEC). IEEE; 20 11. p. 262-8

    work page 2011

  31. [31]

    Artificial bee colony based ima ge clustering method

    Hancer E, Ozturk C, Karaboga D. Artificial bee colony based ima ge clustering method. In: 2012 IEEE congress on evolutionary computation. IEEE; 2012 . p. 1-5

    work page 2012

  32. [32]

    Brain image se gmentation based on firefly algorithm combined with k-means clustering

    Hrosik RC, Tuba E, Dolicanin E, Jovanovic R, Tuba M. Brain image se gmentation based on firefly algorithm combined with k-means clustering. Stud In form Control. 2019;28(2):167-76

    work page 2019

  33. [33]

    A genetic c-means clustering algorithm applied to color image quantiza- tion

    Scheunders P. A genetic c-means clustering algorithm applied to color image quantiza- tion. Pattern recognition. 1997;30(6):859-66. 30

    work page 1997

  34. [34]

    Clustering using firefly algorith m: Performance study

    Senthilnath J, Omkar SN, Mani V. Clustering using firefly algorith m: Performance study. Swarm and Evolutionary Computation. 2011;1(3):164 171. A vailable from: http://www.sciencedirect.com/science/article/pii/S2210650211000265

    work page 2011

  35. [35]

    A new hybrid approach for data c lustering using fire- fly algorithm and K-means

    Hassanzadeh T, Meybodi MR. A new hybrid approach for data c lustering using fire- fly algorithm and K-means. In: The 16th CSI International Sympos ium on Artificial Intelligence and Signal Processing (AISP 2012). IEEE; 2012. p. 00 7-11

    work page 2012

  36. [36]

    Performance analysis of firefly algorithm fo r data clustering

    Banati H, Bajaj M. Performance analysis of firefly algorithm fo r data clustering. Inter- national Journal of Swarm Intelligence. 2013;1(1):19-35

    work page 2013

  37. [37]

    Implementation of energy efficient clustering u sing firefly algorithm in wireless sensor networks

    Sarma N, Gopi M. Implementation of energy efficient clustering u sing firefly algorithm in wireless sensor networks. International Proceedings of Compu ter Science and Infor- mation Technology. 2014;59:1

    work page 2014

  38. [38]

    A hybrid firefly algorithm with fuzzy-C mean algorithm for MRI brain segmentation

    Alsmadi MK. A hybrid firefly algorithm with fuzzy-C mean algorithm for MRI brain segmentation. American Journal of Applied Sciences. 2014;11(9):1 676-91

    work page 2014

  39. [39]

    An improved firefl y fuzzy c-means (F AFCM) algorithm for clustering real world data sets

    Nayak J, Nanda M, Nayak K, Naik B, Behera HS. An improved firefl y fuzzy c-means (F AFCM) algorithm for clustering real world data sets. In: Advanc ed Computing, Networking and Informatics-Volume 1. Springer; 2014. p. 339-48

    work page 2014

  40. [40]

    Segmentatio n of MRI Brain Images Using FCM Improved by Firefly Algorithms

    Alomoush W, sheikh abdullah S, Sahran S, Hussain R. Segmentatio n of MRI Brain Images Using FCM Improved by Firefly Algorithms. Journal of Applied Sciences. 2014 01;14:66-71

    work page 2014

  41. [41]

    A hybrid data clustering using firefly alg orithm based improved genetic algorithm

    Kaushik K, Arora V, et al. A hybrid data clustering using firefly alg orithm based improved genetic algorithm. Procedia Computer Science. 2015;58:24 9-56

    work page 2015

  42. [42]

    Synchronous firefly algorithm for c luster head selection in WSN

    Baskaran M, Sadagopan C. Synchronous firefly algorithm for c luster head selection in WSN. The Scientific World Journal. 2015;2015

    work page 2015

  43. [43]

    Firefly algorithm based clustering technique for Wire- less Sensor Networks

    Manshahia MS, Dave M, Singh S. Firefly algorithm based clustering technique for Wire- less Sensor Networks. In: 2016 International Conference on Wir eless Communications, Signal Processing and Networking (WiSPNET). IEEE; 2016. p. 1273 -6

    work page 2016

  44. [44]

    Image segmentation using firefly algorithm

    Sharma A, Sehgal S. Image segmentation using firefly algorithm . In: 2016 International Conference on Information Technology (InCITe)-The Next Gene ration IT Summit on the Theme-Internet of Things: Connect your Worlds. IEEE; 2016 . p. 99-102

    work page 2016

  45. [45]

    Taiwanese export trade forecasting using firefly a lgorithm based K-means algorithm and SVR with wavelet transform

    Kuo R, Li P. Taiwanese export trade forecasting using firefly a lgorithm based K-means algorithm and SVR with wavelet transform. Computers & Industrial Engineering. 2016;99:153-61. 31

    work page 2016

  46. [46]

    Fireflies can fin d groups for data clustering

    Mizuno K, Takamatsu S, Shimoyama T, Nishihara S. Fireflies can fin d groups for data clustering. In: 2016 IEEE International Conference on Industr ial Technology (ICIT). IEEE; 2016. p. 746-51

    work page 2016

  47. [47]

    Data Clustering Using Improved Fire Fly Algorith m

    Sadeghzadeh M. Data Clustering Using Improved Fire Fly Algorith m. In: Information Technology: New Generations. Springer; 2016. p. 801-9

    work page 2016

  48. [48]

    Protein complex ident ification through Markov clustering with firefly algorithm on dynamic protein–protein in teraction net- works

    Lei X, Wang F, Wu FX, Zhang A, Pedrycz W. Protein complex ident ification through Markov clustering with firefly algorithm on dynamic protein–protein in teraction net- works. Information Sciences. 2016;329:303-16

    work page 2016

  49. [49]

    Improvement of the Firefly-based K-means Clust ering Algorithm

    Zhou L, Li L. Improvement of the Firefly-based K-means Clust ering Algorithm. In: Proceedings of the 2018 International Conference on Data Scien ce; 2018. p. 157-62

    work page 2018

  50. [50]

    Chaotic firefly algorithm-based fuzzy C -means algorithm for segmentation of brain tissues in magnetic resonance images

    Ghosh P, Mali K, Das SK. Chaotic firefly algorithm-based fuzzy C -means algorithm for segmentation of brain tissues in magnetic resonance images. Journ al of Visual Commu- nication and Image Representation. 2018;54:63-79

    work page 2018

  51. [51]

    Data aggregation in wireless sensor ne tworks using firefly algorithm

    Mosavvar I, Ghaffari A. Data aggregation in wireless sensor ne tworks using firefly algorithm. Wireless Personal Communications. 2019;104(1):307-24

    work page 2019

  52. [52]

    Improving K-means c lustering with enhanced firefly algorithms

    Xie H, Zhang L, Lim CP, Yu Y, Liu C, Liu H, et al. Improving K-means c lustering with enhanced firefly algorithms. Applied Soft Computing. 2019;84:10576 3

    work page 2019

  53. [53]

    Automatic data clustering using hy brid firefly particle swarm optimization algorithm

    Agbaje MB, Ezugwu AE, Els R. Automatic data clustering using hy brid firefly particle swarm optimization algorithm. IEEE Access. 2019;7:184963-84

    work page 2019

  54. [54]

    A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wire less sensor net- works

    Pitchaimanickam B, Murugaboopathi G. A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wire less sensor net- works. Neural Computing and Applications. 2020;32(12):7709-23

    work page 2020

  55. [55]

    Improved density peaks clus tering based on firefly algorithm

    Zhao J, Tang J, Shi A, Fan T, Xu L. Improved density peaks clus tering based on firefly algorithm. International Journal of Bio-Inspired Computation. 2 020;15(1):24-42

    work page

  56. [56]

    Combined fuzz y clustering and firefly algorithm for privacy preserving in social networks

    Langari RK, Sardar S, Mousavi SAA, Radfar R. Combined fuzz y clustering and firefly algorithm for privacy preserving in social networks. Expert Syste ms with Applications. 2020;141:112968

    work page 2020

  57. [57]

    A Two-Layer Con trol Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Net work

    Atman MWS, Fikri MR, Priandana K, Gusrialdi A. A Two-Layer Con trol Framework for Persistent Monitoring of a Large Area With a Robotic Sensor Net work. IEEE Access. 2024

    work page 2024

  58. [58]

    CORRELATED EVOL U- TION OF FEMALE NEOTENY AND FLIGHTLESSNESS WITH MALE SPERMATOPHORE PRODUCTION IN FIREFLIES (COLEOPTERA: 32 LAMPYRIDAE)

    South A, Stanger-Hall K, Jeng ML, Lewis SM. CORRELATED EVOL U- TION OF FEMALE NEOTENY AND FLIGHTLESSNESS WITH MALE SPERMATOPHORE PRODUCTION IN FIREFLIES (COLEOPTERA: 32 LAMPYRIDAE). Evolution. 2011;65(4):1099-113. Available from: http://dx.doi.org/10.1111/j.1558-5646.2010.01199.x

    work page doi:10.1111/j.1558-5646.2010.01199.x 2011

  59. [59]

    An integrated approach to region based image retrieval using firefly algorithm and support vector ma- chine

    Kanimozhi T, Latha K. An integrated approach to region based image retrieval using firefly algorithm and support vector ma- chine. Neurocomputing. 2015;151:1099 1111. Available from: http://www.sciencedirect.com/science/article/pii/S0925231214013332

    work page 2015

  60. [60]

    RGB Histogram Based Color Image Se gmentation Using Firefly Algorithm

    Rajinikanth V, Couceiro MS. RGB Histogram Based Color Image Se gmentation Using Firefly Algorithm. Procedia Computer Science. 2015;46:1449 1457. P roceedings of the International Conference on Information and Communication Technologies, ICICT 2014, 3-5 December 2014 at Bolgatty Palace & Island Resort, Koch i, India. Available from: http://www.sciencedirect...

    work page 2015

  61. [61]

    A combined model based on multiple seasonal patterns and modified firefly algorithm for electric al load forecasting

    Xiao L, Shao W, Liang T, Wang C. A combined model based on multiple seasonal patterns and modified firefly algorithm for electric al load forecasting. Applied Energy. 2016;167:135 153. Available from: http://www.sciencedirect.com/science/article/pii/S0306261916300307

    work page 2016

  62. [62]

    A novel hybrid model for hourly global so- lar radiation prediction using random forests technique and firefly a lgorithm

    Ibrahim IA, Khatib T. A novel hybrid model for hourly global so- lar radiation prediction using random forests technique and firefly a lgorithm. Energy Conversion and Management. 2017;138:413 425. Available fr om: http://www.sciencedirect.com/science/article/pii/S0196890417301036

    work page 2017

  63. [63]

    A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy

    Osaba E, Yang X, D ´ ıaz F, Onieva E, Masegosa AD, Perallos A. A Dis crete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper D istri- bution System with Recycling Policy. CoRR. 2016;abs/1604.04146. Av ailable from: http://arxiv.org/abs/1604.04146

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  64. [64]

    Evolutionary Discrete Firefly Algorithm for T ravelling Salesman Problem

    Jati GK, Suyanto. Evolutionary Discrete Firefly Algorithm for T ravelling Salesman Problem. In: Bouchachia A, editor. Adaptive and Intelligent System s. Berlin, Heidel- berg: Springer Berlin Heidelberg; 2011. p. 393-403

    work page 2011

  65. [65]

    A Novel Hybrid Firefly Algorithm fo r Global Opti- mization

    Zhang L, Liu L, Yang XS, Dai Y. A Novel Hybrid Firefly Algorithm fo r Global Opti- mization. 2016 09;11

    work page 2016

  66. [66]

    A hybrid optimizer based on firefly algorithm and particle swarm optimization alg o- rithm

    Xia X, Gui L, He G, Xie C, Wei B, Xing Y, et al. A hybrid optimizer based on firefly algorithm and particle swarm optimization alg o- rithm. Journal of Computational Science. 2018;26:488 500. Availab le from: http://www.sciencedirect.com/science/article/pii/S1877750317303538

    work page 2018

  67. [67]

    A hybrid firefly algorithm and patte rn search technique for automatic generation control of multi area power s ystems

    Sahu RK, Panda S, Padhan S. A hybrid firefly algorithm and patte rn search technique for automatic generation control of multi area power s ystems. Interna- tional Journal of Electrical Power & Energy Systems. 2015;64:9 23 . Available from: http://www.sciencedirect.com/science/article/pii/S0142061514004451. 33

    work page 2015

  68. [68]

    Multiobjective firefly algorithm for continuous optimiza tion

    Yang XS. Multiobjective firefly algorithm for continuous optimiza tion. Engineering with Computers. 2013 Apr;29(2):175-84. Available from : https://doi.org/10.1007/s00366-012-0254-1

    work page doi:10.1007/s00366-012-0254-1 2013

  69. [69]

    Decompositio n- based multi-objective firefly algorithm for RFID network planning wit h un- certainty

    Zhao C, Wu C, Chai J, Wang X, Yang X, Lee JM, et al. Decompositio n- based multi-objective firefly algorithm for RFID network planning wit h un- certainty. Applied Soft Computing. 2017;55:549 564. Available from: http://www.sciencedirect.com/science/article/pii/S1568494617300789

    work page 2017

  70. [70]

    Multi-objective firefly algorithm bas ed on compensation factor and elite learning

    Lv L, Zhao J, Wang J, Fan T. Multi-objective firefly algorithm bas ed on compensation factor and elite learning. Future Generation Computer Systems. 2 018. Available from: http://www.sciencedirect.com/science/article/pii/S0167739X18313955

    work page

  71. [71]

    A comprehensive review of firefly algorithms

    Fister I, Fister Jr I, Yang XS, Brest J. A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation. 2013;13:34-46

    work page 2013

  72. [72]

    Firefly algorithm for discrete opt imization problems: A survey

    Tilahun SL, Ngnotchouye JMT. Firefly algorithm for discrete opt imization problems: A survey. KSCE Journal of Civil Engineering. 2017 Feb;21(2):535-4 5. Available from: https://doi.org/10.1007/s12205-017-1501-1

    work page doi:10.1007/s12205-017-1501-1 2017

  73. [73]

    Continuous versio ns of firefly algorithm: a review

    Tilahun SL, Ngnotchouye JMT, Hamadneh NN. Continuous versio ns of firefly algorithm: a review. Artificial Intelligence Review. 2017 Jul. Available f rom: https://doi.org/10.1007/s10462-017-9568-0

    work page doi:10.1007/s10462-017-9568-0 2017

  74. [74]

    A framework for self -tuning optimization algorithm

    Yang XS, Deb S, Loomes M, Karamanoglu M. A framework for self -tuning optimization algorithm. Neural Computing and Applications. 2013;23(7-8):2051- 7

    work page 2013

  75. [75]

    Why the firefly algorithm works? Nature-Inspir ed Algorithms and Applied Optimization

    Yang XS, He XS. Why the firefly algorithm works? Nature-Inspir ed Algorithms and Applied Optimization. 2018:245-59

    work page 2018

  76. [76]

    Automatic clustering using an improv ed differential evolution algorithm

    Das S, Abraham A, Konar A. Automatic clustering using an improv ed differential evolution algorithm. IEEE Transactions on systems, man, and cybe rnetics-Part A: Systems and Humans. 2007;38(1):218-37. 34

    work page 2007