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) | | | (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 | | | End of DDS and Invoking Algorithm 2 | | (32) | Invoking Algorithm 2 | | (33) | Set | | (34) | end while | | (35) | return and |
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