Features¶
Algorithms¶
NiaPy features more than 30 algorithms. They are categorized as basic, modified, and others.
Basic algorithms¶
Artificial bee colony algorithm
Bat algorithm
Camel algorithm
Cuckoo search
Differential evolution algorithm
Evolution Strategy
Firefly algorithm
Fireworks algorithm
Flower pollination algorithm
Forest optimization algorithm
Genetic algorithm
Glowworm Swarm Optimization
Grey wolf optimizer
Harmony Search Algorithm
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 algorithm
Dynamic population size self-adaptive differential evolution algorithm
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 algorithm
Documentation for the other algorithms can be found here: NiaPy.algorithms.other
.
Functions¶
NiaPy features more than 30 benchmark functions. Documentation for them can be found here: NiaPy.benchmarks
.
Ackley
Alpine - Alpine1 - Alpine2
Bent Cigar
Chung Reynolds
Csendes
Discus
Dixon-Price
Elliptic
Griewank
Happy cat
HGBat
Katsuura
Levy
Michalewicz
Perm
Pintér
Powell
Qing
Quintic
Rastrigin
Ridge
Rosenbrock
Salomon
Schumer Steiglitz
Schwefel - Schwefel 2.21 - Schwefel 2.22
Sphere - Sphere2 -> Sphere with different powers - Sphere3 -> Rotated hyper-ellipsoid
Step - Step2 - Step3
Stepint
Styblinski-Tang
Sum Squares
Trid
Weierstrass
Whitley
Zakharov
Other examples:¶
Using different termination conditions (nFES, nGEN, reference value)
Basic statistics example (min, max, mean, median, std)
Storing improvements during the evolutionary cycle
Custom initialization of initial population