A Plant Propagation Algorithm for Constrained Engineering Optimisation Problems

Algorithm 2

Pseudocode of PPA for constrained optimisation.

(1) Initialization: 𝑔_max← Maximum number of generations; NP ← population size; 𝑟← trial run

(2) if  𝑟≤NP  then

(3)  Create a random population of plants 𝑝𝑜𝑝={𝑋_𝑖∣𝑖=1,2,…,NP}, using (4) and gather the best solutions.

(4) end if

(5) while  𝑟>NP  do

(6)  Use population 𝑝𝑜𝑝_𝑔 formed by gathering all best solutions from previous runs.

   Calculate 𝐼𝑁_𝑗 value for each column 𝑗 of 𝑝𝑜𝑝_𝑔 (see Section 3).

(7) end while

(8) Evaluate the population. In case of 𝑝𝑜𝑝_𝑔 the algorithm does not need to evaluate the population,

(9) Set number of runners, 𝑛_𝑟=3, 𝑛𝑔𝑒𝑛=1,

(10) while (𝑛𝑔𝑒𝑛<𝑔_max) or (𝑛_𝑒𝑣𝑎𝑙<𝑚𝑎𝑥_𝑒𝑣𝑎𝑙) do

(11)  Create Φ:

(12)  for  𝑖=1 to NP  do

(13)   for  𝑘=1 to 𝑛_𝑟  do

(14)    if  𝑟≤NP  then

(15)     if  rand≤𝑃_𝑚  then

(16)      Generate a new solution 𝑋^∗ according to (5);

(17)       Evaluate it and store it in Φ;

(18)     end if

(19)     if  rand≤𝑃_𝑚  then

(20)      Generate a new solution 𝑋^∗ according to (6);

(21)       Evaluate it and store it in Φ;

(22)     end if

(23)    else

(24)     for  𝑗=1: 𝑛  do

(25)      if (𝐼𝑁_𝑗<4) or (rand≤𝑃_𝑚) then

(26)       update the 𝑗th entry of 𝑋_𝑖, 𝑖=1,2,…,NP, according to (7);

(27)      end if

(28)      Evaluate new solution 𝑋^∗ and store it in Φ;

(29)     end for

(30)    end if

(31)   end for

(32)  end for

(33)  Add Φ to current population;

(34)  Sort the population in ascending order of the objective values;

(35)  Update current best;

(36) end while

(37) Return: Updated population.