Research Article

p-Optimality-Based Multiobjective Root System Growth Algorithms for Multiobjective Applications

Algorithm 1

Pseudocode of p-MORSGA.
  (1)1: Initialization.
(2) Iteration = 0;
(3) Initialize P0, the positions of root tip group;
(4) Evaluate the fitness of the group;
(5) Evaluate the global general (GG) of each solution by equations (4)–(6)
(6)2: Do while (unsatisfactory conditions)
(7)3: Auxin concentration calculation
(8) Compute the auxin concentration values of the root tip group by equation (9);
(9) Sort the root tip group in order of descending accumulated auxin concentration;
(10) Divide the root tip group into two subgroups, i.e. the main root group and the lateral root group of equation (10).
(11)4: Root branching
(12) Determine the branch number of each branching root tip by equation (11);
(13)5: Main roots growing
(14) Sort the main root subgroup in order of increasing cumulative auxin concentration;
(15) The first half main roots are hydrotropic according to equation (12);
(16)6: Lateral roots growing
(17) All the lateral root tips perform random walk operation according to equation (13);
(18)7: Dead roots eliminating
(19) Remove the dead root tips (auxin concentration 0) from the root tip group;
(20)8: Generate offspring group
(21) Gather the new generated solution as offspring group Ot;
(22)9: Determine the solutions of the next iteration
(23) Combine parent group and offspring group
(24) Non-dominated sort on Pareto dominance {F1, F2, …}
(25) Add nondominated front Fi into the next group one by one until
(26)  Add the solutions of last nondominated front Fi with the largest p-optimality criterion one by one until .
(27)8: Memorize the best solution to date
(28) Iteration = iteration + 1;
(29)End while
(30)9: Output the best solution achieved.