## 4 Experimental Studies
### Simulation Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Problem** & **Task** & **NSGA-II** & **MO-MPEA** & **EMT-EGT** & **T-EGT** \\ \hline Condition 1 & T1 & \(2.656\times 10^{-1}(3.036\times 10^{-2})\)\% & \(2.269\times 10^{-1}(2.399\times 10^{-3})\)– & \(2.733\times 10^{-1}(1.938\times 10^{-2})\)\% & \(2.753\times 10^{-1}(1.668\times 10^{-2})\) \\ Condition 2 & T1 & \(2.907\times 10^{-1}(2.818\times 10^{-2})\) - & \(2.562\times 10^{-1}(3.114\times 10^{-3})\) & \(3.008\times 10^{-1}(2.538\times 10^{-2})\) & \(3.120\times 10^{-1}(1.992\times 10^{-2})\) \\ Condition 3 & T1 & \(3.346\times 10^{-1}(3.346\times 10^{-2})\) & \(2.999\times 10^{-1}(2.706\times 10^{-3})\) - & \(3.601\times 10^{-1}(3.100\times 10^{-2})\) & \(3.624\times 10^{-1}(2.664\times 10^{-2})\) \\ Condition 4 & T1 & \(3.619\times 10^{-1}(3.322\times 10^{-2})\) & \(3.036\times 10^{-2}(2.780\times 10^{-3})\) & \(3.537\times 10^{-1}(3.135\times 10^{-2})\) & \(3.761\times 10^{-2}(2.651\times 10^{-2})\) \\ Condition 5 & T1 & \(3.339\times 10^{-1}(2.600\times 10^{-2})\) & \(2.792\times 10^{-2}(2.805\times 10^{-3})\) - & \(3.237\times 10^{-1}(2.839\times 10^{-2})\) - & \(3.482\times 10^{-1}(3.380\times 10^{-2})\) \\ Condition 6 & T1 & \(4.363\times 10^{-1}(2.892\times 10^{-2})\) - & \(3.730\times 10^{-1}(1.648\times 10^{-3})\) - & \(4.428\times 10^{-2}(4.244\times 10^{-2})\) = \(4.608\times 10^{-2}(2.838\times 10^{-2})\) \\ Condition 7 & T1 & \(4.660\times 10^{-1}(3.494\times 10^{-2})\) - & \(4.043\times 10^{-1}(4.134\times 10^{-3})\) - & \(4.072\times 10^{-1}(4.309\times 10^{-2})\) & \(5.067\times 10^{-1}(4.459\times 10^{-2})\) \\ Condition 8 & T1 & \(4.442\times 10^{-1}(3.3857\times 10^{-2})\) - & \(3.761\times 10^{-1}(3.0368\times 10^{-3})\) - & \(4.478\times 10^{-2}(3.241\times 10^{-2})\) - & \(4.488\times 10^{-1}(3.380\times 10^{-2})\) \\ Condition 9 & T1 & \(3.759\times 10^{-1}(3.875\times 10^{-2})\) & \(3.227\times 10^{-1}(3.397\times 10^{-3})\) - & \(3.808\times 10^{-2}(2.878\times 10^{-2})\) - & \(3.981\times 10^{-2}(2.673\times 10^{-2})\) \\ Condition 10 & T1 & \(5.036\times 10^{-1}(5.163\times 10^{-2})\) & \(4.196\times 10^{-1}(3.861\times 10^{-3})\) - & \(4.979\times 10^{-1}(4.177\times 10^{-2})\) & \(5.067\times 10^{-1}(2.843\times 10^{-2})\) \\ \hline \end{tabular}
\end{table} 
Table 6: Averaged HV value and Standard Deviation obtained by NSGA-II, MO-MPEA, EMT-EGT, and T-EGT of task\({}_{i}\) on ten conditions. \begin{table}
\begin{tabular}{c c c c c} \hline
**Problem** & **Task** & **NSGA-II** & **MO-MPEA** & **EMT-EGT** & **T-EGT** \\ \hline Condition 1 & T2 & \(2.657\times 10^{-1}(2.602\times 10^{-2})\)\% & \(2.229\times 10^{-1}(2.337\times 10^{-3})\)– & \(2.251\times 10^{-1}(3.685\times 10^{-4})\)– & \(2.678\times 10^{-1}(2.138\times 10^{-2})\) \\ Condition 2 & T2 & \(3.074\times 10^{-1  
The MO-MFEA is generally stagnant and it is easy to get stuck in local optimal regions. Meanwhile, The EMT-EGT and NSGA-II algorithms achieve a good performance may be attributed to formulated PPCFP. It can be also observed that Tr-EGT converges rapidly under all conditions in the early optimization stages, which is attributed to the fact that it employs the Tr-OG strategy to autonomously exploit latent complementarities between two different problems, thereby successfully improving the positive knowledge transfer. Since the proposed Tr-EMT algorithm shares the same evolutionary solver as NSGA-II, the only difference is that the proposed method contains explicit genetic transfer between different tasks, the convergence speed of the average HV numerical curve confirms the effectiveness of performing Tr-EMT for optimization. Moreover, compared to the recently proposed benchmark algorithm MO-MFEA with implicit genetic transfer for multiobjective multitask optimization, the Tr-EMT algorithm obtains superior solution quality in terms of the average HV value across all tasks, which further confirms the effectiveness of the proposed Tr-EMT with explicit genetic transfer across tasks.