The Pareto-optimal sets (POSs) for

The Pareto-optimal sets (POSs) for scenarios 1 and 2 are composed of 5 and 4 optimal solutions respectively (see Fig. 6). The corresponding ETX and end-to-end delay values of all optimal solutions are listed in Table 5. In terms of the multi-objective optimization, two distinct goals should be considered: (1) solutions should be as close as possible to the POS and (2) solutions should be as diverse as possible in the obtained non-dominated set. In terms of the former goal, the set of non-dominated solutions presented were the best among all runs of the SPEA algorithm whose goal was to minimize both objectives (delay and ETX). Fig. 6 shows that solutions from scenario 2 are better distributed across the POS in comparison to those of scenario 1. Despite this, both sets of solutions satisfy the latter goal.

結果 (繁體中文) 1:

帕累托最優設置 (POSs) 方案 1 和 2 分別是由組成的 5 和 4 的最優解 （見圖 6）。表 5 列出了相應的 ETX 和端到端時延值的所有最優解。在多目標優化，應視為兩個不同的目標: （1） 解決辦法應儘量靠近 POS 和 （2） 解決辦法應盡可能在獲得非支配集多樣。在前的目標，提出了一種非劣解集的是說演算法，其目標是儘量減少這兩個目標 （延遲和 ETX） 的所有運行中最好的。圖 6 顯示了更好地將解決方案從場景 2 分佈相對於場景 1 POS。儘管這樣，這兩個組的解滿足後者的目標。

結果 (繁體中文) 2:

帕累托最優集（POSS）為場景1和2分別由5和4的最佳解決方案（參見圖6）。所有最優解的相應ETX和端至端延遲值在表5中列出的多目標優化方面，兩個不同的目標，應考慮：（1）解決方案應該是盡可能接近到POS和（2）的解決方案應該盡可能在所得的非支配集多種多樣。在前者的目標方面，提出了一套非支配解的是該SPEA算法的目標是兩個目標（延遲和ETX）最小化的所有運行中最好的。圖。如圖6所示，從情景2的解決方案在整個POS相比，這些場景1.儘管這更好的分配，解決方案的兩套滿足後期目標。

結果 (繁體中文) 3:

the帕累托最優套（POSS）for are composed of scenarios 1和2 5和4分別優化解決方案（見圖6）。the corresponding ETX和端到端延遲values of all are listed in Table 5優化解決方案。in terms of the多目標優化，兩個不同的目標：（1）should be considered as possible Solutions should be as close to the（2）POS Solutions and should be obtained in the diverse as possible你沒有控制組。in terms of the goal of the former為主，集不選擇among all were presented the best of the SPEA算灋的一個目標是目標（Delay and both to minimize ETX）。圖6 2 shows that的情况是好的溶液從POS distributed across the in comparison to those of情景1。儘管這兩個解決方案，滿足sets of the latter的目標。