NiaPy
¶
Python micro framework for building nature-inspired algorithms.
-
class
NiaPy.
Runner
(D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, A=0.5, r=0.5, Qmin=0.0, Qmax=2.0, Pa=0.25, F=0.5, CR=0.9, alpha=0.5, betamin=0.2, gamma=1.0, p=0.5, Ts=4, Mr=0.05, C1=2.0, C2=2.0, w=0.7, vMin=-4, vMax=4, Tao=0.1)[source]¶ Runner utility feature.
Feature which enables running multiple algorithms with multiple benchmarks. It also support exporting results in various formats (e.g. LaTeX, Excel, JSON)
Initialize Runner.
__init__(self, D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, …)
- Arguments:
D {integer} – dimension of problem
NP {integer} – population size
nFES {integer} – number of function evaluations
nRuns {integer} – number of repetitions
useAlgorithms [] – array of algorithms to run
useBenchmarks [] – array of benchmarks to run
A {decimal} – laudness
r {decimal} – pulse rate
Qmin {decimal} – minimum frequency
Qmax {decimal} – maximum frequency
Pa {decimal} – probability
F {decimal} – scalling factor
CR {decimal} – crossover rate
alpha {decimal} – alpha parameter
betamin {decimal} – betamin parameter
gamma {decimal} – gamma parameter
p {decimal} – probability switch
Ts {decimal} – tournament selection
Mr {decimal} – mutation rate
C1 {decimal} – cognitive component
C2 {decimal} – social component
w {decimal} – inertia weight
vMin {decimal} – minimal velocity
vMax {decimal} – maximal velocity
Tao {decimal}