| (1) | The pseuco code of EFLA ( ) |
| (2) | Parameter setting: population size , m, n Max fitness number (MAX_FES) etc. |
| (3) | Initialize a population of frogs with random solutions and compute the fitness. |
| (4) | while FES< = MAX_FES |
| (5) | //Local search process (exploitation) |
| (6) | sort ; //descending order for the population according to the fitness values |
| (7) | For (i = 1; i < = m; i + +)//m is the number of memeplexes |
| (8) | For (j = 1; j < = n; j + +)//n is the number of frogs in one submemeplex |
| (9) | Get the best frog and the worst frog in one memeplex; |
| (10) | Computing the two potential wells and length of search by equations (8), (9), (10), and (11); |
| (11) | Updating the position of frogs by equation (7); |
| (12) | End for |
| (13) | End for |
| (14) | //Global search process (exploration) |
| (15) | For (i = 1; i< = ; i + +) |
| (16) | Obtain the new position of frogs by equation (12); // means a vector |
| (17) | End for |
| (18) | Use equations (13), (14), (15), (16), and (17); //the eigenvector basis based search operator |
| (19) | For (i = 1; i < = ; i + +)// |
| (20) | If rand < |
| (21) | Xnew1 = Yi;//standard search equation (12) |
| (22) | Else// , means the same matrix with columns and rows |
| (23) | ; //eigenvector search (equations (15), (16), and (17)) |
| (24) | End IF |
| (25) | p = p(1−1/ps0) + (1/ps0) (p1/(p1 + p2)); //equation (18), Initial p0 = 0.5; ps0 = 2 |
| (26) | // is the number of success by according to the fitness value |
| (27) | // is the number of success by according to the fitness value |
| (28) | End for |
| (29) | End While |
