| (1) Input: Objective function and constraints. |
| (2) Initialization |
| (3) Parameters initialization: assign values to , , , , , , , , , , , , , , , , , |
| (4) Population initialization: generate the random values for and of each particle in the feasible domain, |
| calculate the of initial population. |
| (5) Set , () and . |
| (6) Iterations |
| (7) while |
| (8) |
| (9) for to |
| (10) for to |
| (11) Update the velocity of particle by using (1). |
| (12) Update the position of particle by using (2). |
| (13) if or |
| (14) Update the value of by using (17). |
| (15) end if |
| (16) end for |
| (17) end for |
| (18) Calculate by using the (9). |
| (19) Sort the particle population in ascending order and select the particles with better fitness. |
| (20) Generate explosion sparks by using Algorithm 1. |
| (21) Calculate the fitness of explosion sparks and storage the best explosion spark . |
| (22) Generate mutation sparks by using Algorithm 2. |
| (23) Select the individuals from the explosion sparks and mutation sparks by using the selection strategy. |
| (24) Combine the particles with individuals to generate the new population. |
| (25) Calculate and of new population. |
| (26) end while |
| (27) Output: |
