Features¶
Algorithms¶
NiaPy features more than 30 algorithms. They are categorized as basic, modified, and others.
Basic algorithms¶
Artificial Bee Colony
Bacterial Foraging Optimization
Bat Algorithm
Bees Algorithm
Camel Algorithm
Cat Swarm Optimization
Clonal Selection Algorithm
Coral Reefs Optimization Algorithm
Cuckoo Search
Differential Evolution
Evolution Strategy
Firefly Algorithm
Fireworks Algorithm
Fish School Search
Flower Pollination Algorithm
Forest Optimization Algorithm
Genetic Algorithm
Glowworm Swarm Optimization
Gravitational Search Algorithm
Grey Wolf Optimizer
Harmony Search
Harris Hawks Optimization
Krill Herd Algorithm
Monarch Butterfly Optimization
Monkey King Evolution
Moth flame Optimizer
Particle Swarm Optimization
Sine Cosine Algorithm
Documentation for the basic algorithms can be found here: niapy.algorithms.basic
.
Modified algorithms¶
Hybrid Bat Algorithm
Self-adaptive Differential Evolution
Dynamic Population Size Self-adaptive Differential Evolution
Documentation for the modified algorithms can be found here: niapy.algorithms.modified
.
Other algorithms¶
Anarchic Society Optimization
Hill Climb algorithm
Multiple Trajectory Search
Nelder Mead Method
Simulated Annealing
Documentation for the other algorithms can be found here: niapy.algorithms.other
.
Functions¶
NiaPy features more than 30 optimization test problems. Documentation for them can be found here: niapy.problems
.
Ackley
- Alpine
Alpine1
Alpine2
Bent Cigar
Chung Reynolds
Cosine Mixture
Csendes
Discus
Dixon-Price
Elliptic
Griewank - Expanded Griewank plus Rosenbrock
Happy cat
HGBat
Katsuura
Levy
Michalewicz
Perm
Pintér
Powell
Qing
Quintic
Rastrigin
Ridge
Rosenbrock
Salomon
Schaffer - Schaffer N. 2 - Schaffer N. 4 - Expanded Schaffer
Schumer Steiglitz
- Schwefel
Schwefel 2.21
Schwefel 2.22
Modified Schwefel
- Sphere
Sphere2 -> Sphere with different powers
Sphere3 -> Rotated hyper-ellipsoid
- Step
Step2
Step3
Stepint
Styblinski-Tang
Sum Squares
Trid
Weierstrass
Whitley
Zakharov
Other features¶
Using different termination conditions (function evaluations, number of iterations, cutoff value)
Storing improvements during the evolutionary cycle
Custom initialization of initial population