| 1. Create a population of random solutions and corresponding speeds . |
| 2. Initialize an external archive and define a memory of flight experience for each individual, |
| i.e. . |
| 3. Evaluate using the multi-objective functions. |
| 4. Find the non-dominated solutions and store them in . |
| 5. Develop hypercubes of the search space and distribute the individuals within the hypercubes |
| using the multi-objective function values. |
| 6. Apply TOPSIS on to determine the leader solution or the global best individual . |
| 7. While stopping criteria are not satisfied do: |
| a) Generate a new population A by using LFs using a modified version of Eq. (9) as: |
| |
| |
| b) Evaluate all particles in A, compare A with using the non-domination criterion |
| and update . |
| c) Generate a new population B by using PSO as: |
| |
| |
| where is the current particle; is the particle’s velocity; is the inertia factor; |
| and are acceleration coefficients; and imply random numbers ; is the |
| current generation number. |
| d) Evaluate all particles in , compare with using the non-domination criterion |
| and update . |
| e) Remove the non-dominated solutions and fill the empty positions with randomly |
| created solutions as: |
| |
| where is a Gaussian random permutation operator; is the |
| removed dominated solution; is a random number . |
| f) Evaluate all particles in and insert the non-dominated solution into . |
| g) Update the hybercubes in the current |
| h) Remove the extra individuals in by eliminating the crowded individuals within the |
| corresponding hypercubes. |
| i) Apply TOPSIS on to determine the global best individual . |
| j) Update for all individuals. |
| 8 End. |
