opt_mo: Optimization

In vodopijaaljosa/cyclone: Cyclone Toolbox

Description

This function solves the cyclone optimization problem

Usage

opt_mo(problem, method = c("nsga2", "demo"), control = NULL, no.runs = 1)

Arguments

problem	A list representing the problem configuration: lower.bounds Lower box constraints. upper.bounds Upper box constraints. cons Constraints considered in optimization.
method	A string indicating the used optimization algorithm. It can be NSGA-II ("nsga2"), DEMO ("demo") or MOEA/D ("moead"). Default is DEMO.
control	A list of control parameters: #' pop.size An integer denoting the number of solutions used by the optimization algorithm. Default is 100. no.iters An integer denoting the number of iterations (generations) used by the optimization algorithm. Default is 100. cross.prob A float from [0,1] denoting the crossover probability. Default is 0.9. mut.prob A float from [0,1] denoting the mutation probability. For DEMO this is the scaling factor. Default is 0.1 for NSGA-II and MOEA/D, while 0.7 for DEMO. Default values are used for unspecified parameters.
no.runs	An integer denoting the number of runs. Default is 1.

Value

List of data frames storing the decision varaibles (x) and objective values (y) of all nonodominated feasible solutions found during each run.

vodopijaaljosa/cyclone documentation built on Jan. 4, 2021, 7:09 a.m.