Python micro framework for building nature-inspired algorithms.

class NiaPy.Runner(D, NP, nFES, nRuns, useAlgorithms, useBenchmarks, **kwargs)[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

F_l {decimal} – lower limit of scalling factor

F_u {decimal} – upper limit of 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

Tao1 {decimal} –

Tao2 {decimal} –

n {integer} – number of sparks

mu {decimal} – mu parameter

omega {decimal} – TODO

S_init {decimal} – initial supply for camel

E_init {decimal} – initial endurance for camel

T_min {decimal} – minimal temperature

T_max {decimal} – maximal temperature

C_a {decimal} – Amplification factor

C_r {decimal} – Reduction factor

Limit {integer} – Limit

k {integer} – Number of runs before adaptive