# IDEINFO: An Improved Vector-Weighted Optimization Algorithm
## 5 Conclusions  
This study proposed an improved INFO algorithm that overcomes the shortcomings of the traditional version. The new version initializes candidate solutions using a two-stage backward learning strategy, which improves the uniformity of their distribution and enhances the search capability of the algorithm. The new INFO algorithm is augmented by combining it with a greedy search algorithm, which results in improved individual vectors per iteration and an improved search capacity. During the iterative search process, a DE strategy is applied to perturb the vectors and generate genetically superior candidate solutions. Furthermore, the search range is expanded probabilistically and combined with a \(t\)-distribution strategy, which helps avoid local optima traps and improves the global search capability. Using fourteen standard test functions, the improved INFO outperformed the baseline INFO, SSA, GWO, and WOA models. To further verify the efficacy of the improvement points, comparisons were made with INFO1, which performs only two-stage backward learning and a greedy mechanism, and INFO2, which only performs the combined DE and adaptive \(t\)-distribution actions. The ablative results show that the proposed improved INFO algorithm has better generality, whereas the others present limitations. In the future, we plan to determine how to balance the time and optimization capabilities of the new algorithm, improve its stability, and find practical applications. The convergence rate of IDEINFO proposed in this paper is very impressive because the position of the vectors always tends to move toward the region where a better solution is available. In addition, the IDEINFO algorithm can solve practically complex and challenging optimization problems with constrained and unknown search domains.  
For future research, we suggest the following corrections and considerations. Moreover, we suggest enhancing INFO using different types of local search operators in the original INFO; this may further improve the algorithm's optimality seeking ability and its capability to address the challenging nature of complex scenarios. The IDEINFO algorithm proposed in this paper can also be enriched in terms of exploratory and exploitative trends. For example, using different concepts, such as chaotic mapping, could enrich the exploratory and exploitative trends of the proposed IDEINFO. The traditional INFO, or its improved variants, can be applied to applications such as parameter tuning in neural network models, effect enhancement of model prediction methods, and deep learning.  
Conceptualization, H.J.; methodology, L.Z.; software, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**F** & **Index** & **INFO** & **SSA** & **GWO** & **WOA** & **INFO1** & **INFO2** \\ \hline \multirow{3}{*}{F10} & P & 0.00018267 & 0.021134 & 0.14047 & 0.00018267 & 0.00018267 & 0.8796 \\ \cline{2-7}  & R & **+** & **+** & **-** & **+** & **+** & **-** \\ \hline \multirow{3}{*}{F11} & P & 0.00017562 & 0.00017562 & 0.00017562 & 0.00017562 & 0.00017562 & 0.010336 \\ \cline{2-7}  & R & **+** & **+** & **+** & **+** & **+** & **+** \\ \hline \multirow{3}{*}{F12} & P & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & NaN \\ \cline{2-7}  & R & **+** & **+** & **+** & **+** & **+** & **=** \\ \hline \multirow{3}{*}{F13} & P & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & 6.3864 \(\times 10^{-5}\) & NaN \\ \cline{2-7}  & R & **+** & **+** & **+** & **+** & **+** & **=** \\ \hline \multirow{3}{*}{F14} & P & 0.00010997 & 0.00010997 & 0.00010997 & 0.00010997 & 0.00010997 & 0.58278 \\ \cline{2-7}  & R & **+** & **+** & **+** & **+** & **+** & **-** \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._