Comparison of a Fuzzy Genetic and Simulated Annealing Algorithm Approach for Project Time-Cost Tradeoff

Algorithm 1

Genetic algorithm pseudocode for maxmin approach.

Algorithm for solving fuzzy optimization model

 /* Initialization */

(01) set 𝑁_𝑐 = number constraints

(02) select an initial state 𝑥

  /* Membership determination */

(03)  for  𝑖=1  to  𝑁_𝑐

(04)   µ(𝑖):= membership value of the constraint 𝑅_𝑖(𝑥)

(05) min1:= aggregation(µ(1), …, µ(𝑁_𝑐))

 /* Genetic Algorithm */

(06)  set numberOfGeneration = 0

(07)  repeat

(08)    repeat

(09)       createNextGeneration

(10)         for 𝑖=1  to  𝑁_𝑐

(11)         µ(i):= membership value of the constraint 𝑅_𝑖(𝑥)

(12)       min2 = aggregation(µ(1), …, µ(𝑁_𝑐))

(13)       calculate delta = min2 − min1

(14)       if delta > 0 then

(15)         UpdateNextPopulation;

(16)       min2 = min1;

(17)    until not terminate(𝑦, evaluate(𝑦), θ(𝑘))

(19)  numberOfGeneration++;

(18)   until stop criteria