Research Article
Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization
| Inputs: Scalar neighborhood size perturbation factor , maximum number of iterations , number of variables (dimension) , upper bounds and lower bounds | | Outputs: and | (1) | Initialization | | , | | Set k = 1, , , | (2) | while do | (3) | Compute the probability of perturbing the decision variables using equation (1) | (4) | for to do | (5) | Generate uniform random numbers, | (6) | if then | (7) | Set | (8) | end if | (9) | end for | (10) | Generate a standard normal random numbers, | (11) | for to do | (12) | //equation (2) | (13) | end for | (14) | for to do | (15) | if then | (16) | Set | (17) | if then | (18) | Set | (19) end if | (20) | end if | (21) | if then | (22) | Set | (23) | if then | (24) | Set | (25) | end if | (26) | end if | (27) | end for | (28) | Evaluate | (29) | if then | (30) | Set , | (31) | end if | (32) | Set | (33) | end while |
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