# Multi-Strategy-Improvement-Based Slime Mould Algorithm
## 5 Conclusions  
The MSSMA uses four different strategies to improve the SMA algorithm: (1) using chaotic mapping and inverse learning to generate a better initial population and improve the convergence accuracy; (2) proposing a balancing factor B based on the fitness value order, and according to B, dividing the population into two parts that are searched globally and locally (this ensures that the individuals with poor fitness values are searched globally in the early stage of iteration, and locally around the individuals' optima in the later stage of iteration, which better maintains the balance between exploration and exploitation of the algorithm); (3) introducing the improved Levy flight strategy, which performs random searches around the individuals of the initial population that are obtained to be more optimal, and has a better chance of discovering the optimal solution than the random generation strategy; and (4) using the elite tangent search strategy to perform larger-scale local searches around the individual when it falls into local optimality, which accelerates the convergence of the algorithm and improves the ability to jump out of local optimality.  
Compared with the other five algorithms such as SMA, the MSSMA achieved better experimental results in a test set of 50 benchmark test functions, which proves the effectiveness of the improved strategy. In addition, the MSSMA performed better in terms of the stability of planning path optimization with better paths in collaborative multi-UAV path planning, which is expected to have a wide range of applications in collaborative multi-UAV path planning problems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Minimum Cost** & **Mean Cost** & **Var** \\ \hline MSSMA & 53.641 & 103.123 & 74.996 \\ SMA & 127.708 & 143.431 & 106.813 \\ WOA & 183.296 & 247.842 & 245.518 \\ KO & 136.166 & 196.649 & 305.482 \\ BWO & 2350.998 & 2421.506 & 1558.914 \\ IVY & 213.893 & 265.483 & 81.260 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results of a collaborative multi-UAV 3D path plan.  
Figure 5: Threat areas and pathways planned by MSSMA.  
**Author Contributions:** Conceptualization, D.H. and T.T.; methodology, D.H.; software, D.H.; validation, D.H., T.T. and Y.Y.; formal analysis, D.H.; investigation, D.H.; resources, D.H.; data curation, D.H.; writing--original draft preparation, D.H.; writing--review and editing, D.H.; visualization, D.H.; supervision, T.T.; project administration, T.T.; funding acquisition, T.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 62266004. Also, this research was funded by the National Natural Science Foundation of China, grant number 72462006.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data underlying this article will be shared upon reasonable request made to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.