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, …)


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}