Evaluation of vectorized objective function returned a malformed matrix

[swaheera]

I am working with the R programming language. I am using the "mco" library to perform "constrained multi-objective optimization" (https://cran.r-project.org/web/packages/mco/mco.pdf)

#load libraries
library(dplyr)
library(mco)

# create some data for this example
    a1 = rnorm(1000,100,10)
    b1 = rnorm(1000,100,5)
    c1 = sample.int(1000, 1000, replace = TRUE)
    train_data = data.frame(a1,b1,c1)

I defined the following function that I want to optimize (7 inputs, 4 outputs):

#define function

funct_set <- function (x) {
    x1 <- x[1]; x2 <- x[2]; x3 <- x[3] ; x4 <- x[4]; x5 <- x[5]; x6 <- x[6]; x[7] <- x[7]
    f <- numeric(7)
    
    
    #bin data according to random criteria
    train_data <- train_data %>%
        mutate(cat = ifelse(a1 <= x1 & b1 <= x3, "a",
                            ifelse(a1 <= x2 & b1 <= x4, "b", "c")))
    
    train_data$cat = as.factor(train_data$cat)
    
    #new splits
    a_table = train_data %>%
        filter(cat == "a") %>%
        select(a1, b1, c1, cat)
    
    b_table = train_data %>%
        filter(cat == "b") %>%
        select(a1, b1, c1, cat)
    
    c_table = train_data %>%
        filter(cat == "c") %>%
        select(a1, b1, c1, cat)
    
    
    
    #calculate  quantile ("quant") for each bin
    
    table_a = data.frame(a_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[5],1,0 )))
    
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[6],1,0 )))
    
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = ifelse(c1 > x[7],1,0 )))
    
    f[1] = mean(table_a$quant)
    f[2] = mean(table_b$quant)
    f[3] = mean(table_c$quant)
    
    
    #group all tables
    
    final_table = rbind(table_a, table_b, table_c)
    # calculate the total mean 
    
    f[4] = mean(final_table$quant)
    
#count number of rows in each table
n5 = data.frame(table_a %>% 
  summarise(count = n()))

n6 = data.frame(table_b %>% 
  summarise(count = n()))

n7 = data.frame(table_c %>% 
  summarise(count = n()))



    
    return (f);
}

Next, I defined a series of constraints:

gn <- function(x,f) {
    g1 <- x[3] - x[1] 
    g2<- x[4] - x[2] 
    g3 <- x[7] - x[6]
    g4 <- x[6] - x[5] 
 g5 <- n5 > 20
 g6 <- n6 > 20
 g7 <- n7 > 20
    return(c(g1,g2,g3,g4, g5, g6, g7))
}

Finally, I ran the optimization algorithm (idim = input dimension, odim = output dimension, cdim = dimension of the constraints)

optimization <- nsga2(funct_set, idim = 7, odim = 4 , constraints = gn, cdim = 7,
                      
                      generations=150,
                      popsize=100,
                      cprob=0.7,
                      cdist=20,
                      mprob=0.2,
                      mdist=20,
                      lower.bounds=rep(80,80,80,80, 100,200,300),
                      upper.bounds=rep(120,120,120,120,200,300,400)
)

Problem: Unfortunately, this produces an error:

Evaluation of vectorized objective function returned a malformed matrix. Expected 4 rows and 100 columns but got 7 rows and 100 columns.

Does anyone know why this error is being produced? Is it because the "gn" object can not accept inputs such as "n5, n6, n7"?

Thanks