# Swarm Intelligence Algorithms for Feature Selection: A Review

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## Abstract

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## Featured Application

**The paper analyzes the usage and mechanisms of feature selection methods that are based on swarm intelligence in different application areas.**

## Abstract

## 1. Introduction

- We performed a comprehensive literature review of swarm intelligence algorithms and propose an SI taxonomy based on the algorithms’ biological inspiration. For all the reviewed algorithms we provide the year of its first appearance in scientific literature; the chronological perspective of SI evolution is presented in this manner.
- We provide unified SI framework, which consists of five fundamental phases. Although the algorithms use different mechanisms, these five phases are common for all of them. The SI framework is used to explain different approaches to FS.
- We provide a comprehensive overview of SI algorithms for FS. Using the proposed SI framework, different approaches, techniques, methods, their settings and implications for use are explained for various FS aspects. Furthermore, the most frequent datasets used for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. Differences among EA approaches against SI approaches are also outlined.
- We systematically outline guidelines for researchers who would like to use SI approaches for FS tasks in real-world.

## 2. Swarm Intelligence

- Initialize population
- Define stop condition
- Evaluate fitness function
- Update and move agents
- Return the global best solution

## 3. Feature Selection

- filter models;
- wrapper models; and
- embedded models.

## 4. Methodology

## 5. Swarm Intelligence for Feature Selection

- Select a subset of features.
- Evaluate the selected subset by performing the selected classification algorithm.

#### 5.1. Agent Representation

#### 5.2. Initialize Population

#### 5.3. Defined Stopping Condition

#### 5.4. Fitness Function

n | is the number of selected features, |

N | is the number of all features, |

${w}_{1}$ | is the weight for classification accuracy, |

${w}_{2}$ | is the weight for selected features. |

#### 5.5. Update and Move Agents

## 6. Applications of Feature Selection with Swarm Intelligence

#### 6.1. Datasets

#### 6.2. Application Areas

- Bio-Medical Engineering
- Electrical and Electronic Engineering
- Computer Engineering

## 7. Discussion

#### 7.1. Issues and Open Questions

#### 7.2. Guidelines for SI Researchers

## 8. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A. Swarm Intelligence Algorithms Sorted by Name

Algorithm | Full Name | Reference |
---|---|---|

ABC | Artificial Bee Colony Algorithm | [112] |

ABO | African Buffalo Optimization | [113] |

ACO | Ant Colony Optimization | [8] |

AFSA | Artificial Fish Swarm Algorithm | [114] |

ALO | Antlion Optimizer | [115] |

BA | Bat Algorithm | [116] |

BA* | Bees Algorithm | [117] |

BA** | Beaver Algorithm | [118] |

BBMO | Bumble Bees Mating Optimization | [119] |

BCO | Bee Colony Optimisation | [120] |

BCO* | Bacterial Colony Optimization | [121] |

BCPA | Bee Collecting Pollen Algorithm | [122] |

BFO | Bacterial Foraging Optimization | [123] |

BH | BeeHive | [124] |

BLA | Bees Life Algorithm | [125] |

BMO | Bird Mating Optimizer | [126] |

BS | Bee System | [127] |

BSO | Bees Swarm Optimization | [128] |

CaTS | The Cat Swarm Optimization | [129] |

CS | Cuckoo Search | [130] |

CSO | Chicken Swarm Optimization | [131] |

DA | Dragonfly Algorithm | [132] |

DPO | Dolphin Partner Optimization | [133] |

FA | Firefly Algorithm | [134] |

FCA | Frog Calling Algorithm | [135] |

FFOA | Fruit Fly Optimization Algorithm | [136] |

FMHBO | Fast Marriage in Honey Bees Optimization | [137] |

FSA | Fish-Swarm Algorithm | [138] |

FSS | Fish School Search | [139] |

GEA | Group Escaping Algorithm | [140] |

GOA | Grasshopper Optimisation Algorithm | [141] |

GSO | Glow-worm Swarm Optimization | [142] |

GWO | Grey Wolf Optimizer | [143] |

HBA | Honey Bee Algorithm | [144] |

HBF | Honey Bee Foraging | [145] |

HBMO | Honey Bees Mating Optimization | [146] |

HS | Hunting Search Algorithm | [147] |

KH | Krill Herd Algorithm | [148] |

MBO | Marriage in Honey Bees Optimization | [149] |

MFO | Moth-Flame Optimization | [150] |

MHSA | Mosquito Host-Seeking Algorithm | [151] |

MiBO | Migrating Birds Optimization | [152] |

PSO | Particle Swam Optimization | [7] |

RIO | Roach Infestation Optimization | [153] |

SFLA | Shuffled Frog Leaping Algorithm | [154] |

SMOA | Slime Mould Optimization Algorithm | [155] |

SSA | Shark-Search Algorithm | [156] |

SuA | Superbug Algorithm | [157] |

TA | Termite Algorithm | [158] |

VBA | Virtual Bee Algorithm | [159] |

WCA | Wolf Colony Algorithm | [160] |

WOA | Whale Optimization Algorithm | [161] |

WPS | Wolf Pack Search Algorithm | [162] |

WSO | Wasp Swarm Optimization | [163] |

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**Figure 2.**Swarm intelligence algorithms. * are added because the same abbreviations are used for different algorithms (see Table A1 in the Appendix A).

**Table 1.**Hits for “swarm intelligence” AND “feature selection” in online academic databases from 2007 to 2017.

Academic Online Database | Hits |
---|---|

ACM Digital Library | 61 |

IEEE Xplore | 52 |

ScienceDirect | 409 |

SpringerLink | 603 |

Web of Science | 98 |

Small | Large | Mixed | ∑ | |
---|---|---|---|---|

Insect | ABC [38,41] | DA [46] | ABC [54] | 11 |

BBMO [44,63] | GSO [50] | FA [42] | ||

ACO [35] | ACO [62] | |||

WSO [73] | ||||

Bacteria | BFO and BCO [64] | 1 | ||

Bird | PSO [29,31] | PSO [66,74,75] | PSO [28,30,76] | 10 |

[77] | [78] | |||

Mammal | GWO [39] | GWO [34,40] | 6 | |

CaTS [65] | BA [55] | |||

WOA [67] | ||||

Fish | AFSA [51,52] | 3 | ||

FSA [79] | ||||

Frog | SFLA [45] | 1 | ||

Other | KH [68,80] | 2 |

Bio-Medical Engineering | Electrical and Electronic Engineering | Computer Engineering | ∑ | |
---|---|---|---|---|

Insects | DA [46] RIO [49] | FA [56] | ABC [33,41] | 13 |

GSO [50] | FA [53] | |||

FA [58,81] | ACO [82] | ACO [83] | ||

ACO [37,57] | ||||

Bacteria | BFO [84] | BFO and BCO [64] | 3 | |

BMA [85] | ||||

Bird | PSO [66,74,75,86] | PSO [87] | PSO [88,89] | 10 |

[76,90,91] | ||||

Mammal | GWO [92,93] | GWO [43] | BA [47] | 5 |

GWO [94] | ||||

Fish | FSS [95] | AFSA [96] | 2 | |

Frog | SFLA [45,97,98] | 3 | ||

Other | KH [99] | KH [100] | 2 |

Algorithm | Publications | Citations | Share |
---|---|---|---|

PSO | 16 | 766 | 46.62% |

ACO | 6 | 226 | 13.76% |

BA | 2 | 154 | 9.37% |

ABC | 4 | 152 | 9.25% |

GWO | 7 | 123 | 7.49% |

FA | 5 | 49 | 2.98% |

WSO | 1 | 26 | 1.58% |

Others | 23 | 147 | 8.95% |

Algorithm | Presentation; Initialization | Definition of Stop Condition | Evaluation | Move | Validation | Advantages |
---|---|---|---|---|---|---|

ABC [38] | binary; forward search strategy | 100 iterations | acc (SVM) | with the pertubation frequency | 10-fold CV | + outperforms PSO, ACO, and GA + on average smaller number of selected features |

ABC [54] | binary; random | 50 iterations | classification error rate (5NN) | use of mutation, recombination and selection | 70% training set, 30% test set, 10-fold CV on training set | + introduced DE based neighborhood mechanism for FS + method removes redundant features effectively and is the most stable and consistent |

ACO [35] | graph | 50 iterations | acc (1NN) | graph search with the probabilistic transition rule | 60% training set, 40% test set | + ants have the authority to select or deselect visiting features; + method permits ants to explore all features; + ants can see all of the unvisited features simultaneously |

PSO [75] | continuous; random | different tests | combined function with acc(adaptive KNN) and number of selected features | based on inertia weight, social and cognitive component | different CV techniques | + high classification accuracy, small muber of selected features, and lower computing time |

GWO [92] | binary; random | 100 iterations | combined function with acc(KELM) and number of selected features | based on alpha, beta, and delta wolves | 10-fold CV | + method adaptively converges more quickly, produces much better solution quality, but also gains less number of selected features, achieving high classification performance |

WOA [67] | binary; random | 100 iterations | combined function with acc (5NN) and number of selected features | based on neighbours | CV | + method combined with the Simulated annealing following two hybrid models: LTH and HRH |

AFSA [52] | binary; random | lack of changes in the optimal features subset for a period of 1 h | acc (SVM) | execution of the three search steps (follow, swarm, and prey) | 10-fold CV | + experiment with and without parameter optimization + dynamic vision proposed to assign an appropriate search space for each fish |

SFLA [45] | binary; random with two equations | 50 iterations | combined function with acc (1NN) and number of selected features | with equations containing adaptive transition factor | 10-fold CV | + provided three improvement strategies |

KH [68] | binary; random | 100 iterations | acc (OPF) | with Sigmoid function | 5-fold CV | + fast method |

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Brezočnik, L.; Fister, I.; Podgorelec, V.
Swarm Intelligence Algorithms for Feature Selection: A Review. *Appl. Sci.* **2018**, *8*, 1521.
https://doi.org/10.3390/app8091521

**AMA Style**

Brezočnik L, Fister I, Podgorelec V.
Swarm Intelligence Algorithms for Feature Selection: A Review. *Applied Sciences*. 2018; 8(9):1521.
https://doi.org/10.3390/app8091521

**Chicago/Turabian Style**

Brezočnik, Lucija, Iztok Fister, and Vili Podgorelec.
2018. "Swarm Intelligence Algorithms for Feature Selection: A Review" *Applied Sciences* 8, no. 9: 1521.
https://doi.org/10.3390/app8091521