A Case Study of an Optimal Detailed Analysis of a Standalone Photovoltaic/Battery System for Electricity Supply in Rural and Remote Areas

Table 2

Pseudo-code of the multiobjective firefly optimization algorithm [29].

---

Defining the objective functions

Initializing a population of n fireflies

while k ≤ maximum number of iterations

 for i = 1: n

  for j = 1:n (i ≠ j)

Evaluate the approximations Y_i and Y_j

if Y_i pareto dominates Y_j

 Move firefly i towards j
 Generate new solutions

Endif

 if no non-dominated solutions can be found

 Generate random weights

 Find the best solution

 Random walk around the best solution found

Endif
 Update and pass the non-dominated solutions to next iterations

end for

end for

 Sort and find the current best approximation to the pareto front
 Update k ← k + 1

end while

Results

---