The phrase "average solutions" is correct and usable in written English.
It can be used when discussing solutions that are typical or standard, often in a mathematical or problem-solving context.
Example: "In our analysis, we found that the average solutions provided by the team were effective but lacked innovation."
Alternatives: "typical solutions" or "standard solutions".
Most of the earlier studies on this subject have been based on assumed displacement fields with assumed stress distributions, which usually lead to "average" solutions.
The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions.
Generally, the performance of the algorithms depended on the specific scenarios, however it was found that DE provided the best average solutions in all scenarios investigated in this work.
In addition, the second item above can be partially seen within the first subindex when assessing average solutions per case.
The understandability criterion relates to the missing values index and to the average solutions per case index.
Results are plotted in Figure 5, where the DCCF points show how far the average solutions are from the optimal ones.
It also provides a competitive performance in terms of average solution.
The solution efficiency is greatly improved when using TDP-S location method (the average solution speed increase is 73.9%).
Notably, the crystal structure of the native LacI oDNA complex differs significantly from the average solution conformation.
Open modes work best when the spread between the ideal solution and the average solution is not big and the consequences of missing out on a much better solution from an elite player are small.
The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort.
