Page 15 - Big Data Analytics for Intelligent Healthcare Management

1.3 BIO-INSPIRED ALGORITHMS FOR BIG DATA ANALYTICS: A TAXONOMY 5

Bio-inspired algorithms for big data analytics

Evolutionary Swarm-based Ecological
2015/16
2014 2018
Particle swarm optimization (PSO) [19] [22]
Genetic algorithm (GA) [25] Biogeography based optimization (BBO) [10]
FoS: Group scheduling
FoS: Group scheduling FoS: Multilayer perceptron training
2018
2017 Artificial bee colony (ABC) [13] 2016
Simulated annealing (SA) [16] FoS: Clustering of data Invasive weed colony (IWC) [9]
FoS: Feature selection FoS: Fuzzy normalization
2016
2016 Shuffled frog leaping (SPL) [31]
2016
Cuckoo search optimization (CO) [24] FoS: Biomedical data
Multi-species optimizer (PS2O) [20]
FoS: Convergence stability
2017 FoS: Data selection mining
Fish swarm optimization (FSW) [32]
2016
FoS: Fault tolerance
Evolutionary strategy (ES) [30]
FoS: Cloud resources 2014
Intelligent water drops (IWD) [33]
2016
FoS: Workflow scheduling
Genetic programming (GP) [29]
FoS: Concrete creep model 2017/14
Bacterial foraging optimization (BFO) [34] [35]
2016
FoS: Intrusion prevention and detection
Differential evolution (DE) [28]
2014
FoS: Local search
FSO hybrid (FSOH) [15]
FoS: Execution cost
2017
Artificial immune system (AIS) [8]
FoS: Internet traffic data
2015
Firefly swarm optimization (FSO)
[14]
FoS: Social network
2015
Group searcher optimization (GSO) [36]
FoS: Data clustering
2016
Cat swarm optimization (CSO)
[21]
FoS: Text classification
2016
Swarm intelligence (SI)
[23]
FoS: Load dispatcher
2018/16
Ant colony optimization (ACO)
[26] [27]
FoS: Mobile and medical big data
FIG. 1.5
Taxonomy of bio-inspired algorithms for big data analytics.