Open Access
Table 1
Algorithm: Pseudo-code of QOCNNA.
| Algorithm: Pseudocode of QOCNNA |
|---|
| Initialize the initial parameters of the QOCNNA (Nr, Tmax, Jr, and K) |
| % QOBL-based population initialization % |
| Randomly generate an initial population X |
| Generate quasi-opposite solutions QOX of initial pattern solutions X |
| Calculate the fitness function values of the combined set {X,QOX} and sort them |
| Select the Nr best solutions from the set {X,QOX} as the initial population |
| Randomly generate the weight matrix considering the imposed constraints in equation (39) |
| Define target solution Xtarget and its corresponding target weight (Wtarget ) |
| for t=1: Tmax |
| % Search process of NNA % |
| Generate new pattern solutions (Xnew) using equations (41) and (42) |
| Update the weight matrix (W) using equation (43) |
| for i=1: Nr |
| if rand<β |
| Perform the bias operator |
| else (rand≥β) |
| Apply the transfer function operator |
| end if |
| end for |
| Calculate the fitness function values for all updated pattern solutions |
| Update the value of β\betaβ |
| % QOBL-based generation jumping % |
| if rand< Jr |
| Generate quasi-opposite solutions (QOX) of updated pattern solutions Xt |
| Calculate the fitness function values for quasi-opposite solutions (QOX) |
| if fi(QOXt )<fi(Xt ) |
| Xt = QOXt |
| fi(QOXt )<fi(Xt ) |
| end if |
| end if |
| % Chaotic local search % |
| Update the target solution and its corresponding target weight |
| Perform the CLS strategy to generate a better target solution |
| end for |
| Return the target solution Xtarget and its corresponding fitness function value |
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