# Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization
## 4 Experimental Results and Analysis
### Experiment 3  
In this subsection, the benchmark functions in Experiment 2 are reused. The parameter setting is adopted, consistent with Experiment 2 (i.e., 30 population size and 1000 iterations). The properties of the proposed WMSSA are compared against other based-swarm optimization algorithms, namely the antlion optimizer (ALO) [14], moth-flame optimization (MFO) [30], and the grasshopper optimization algorithm (GOA) [31]. The numerical results for several statistics, e.g., mean and criterion deviation of objective functions, are collected in Table 4. A comparison among the proposed WMSSA, classic SSA, MFO, GOA, and ALO for the convergence speed of the optimum obtained method is illustrated in Figure 7.  
The result of the objective functions ground on average and criterion deviation are exhibited in Table 4. The table demonstrated that for the unimodal and multimodal functions, the mentioned WMSSA is beyond all other algorithms (including the classic SSA, although excluding MFO in problem F5). Although the WMSSA and other swarm optimization algorithms have similar mean values, WMSSA is still superior to other algorithms for F6. The analysis of the standard deviation also demonstrates the better efficiency and stability of the WMSSA. Especially for multimodal functions (F6 to F10), it can also be noticed from the table that the WMSSA has a dramatic improvement to local optima avoidance capacity.  
Compared with the classic SSA, the aforementioned WMSSA has significant advantages, as shown in Table 4. In addition, for multimodal functions with massive number of optima, it is capable of exploring the entire search area and avoid falling into the local optima by implementing WMSSA.  
Figure 7 indicates that the proposed WMSSA exceeds other algorithms in accordance to the convergence speed on functions F1, F3-F4, F6-F7, and F9-F10. On the test functions F2, F5, and F8, although the WMSSA is slightly worse than the MFO, it is still significantly better than the SSA. Besides, in accordance with Table 4, the results show that the proposed WMSSA has stronger stability than the MFO. Due to the complexity the multimodal function F6, though the consequences of the comparison algorithms are similar, the linear coordinates show that the proposed WMSSA can obtain preferable optimal solution. The proposed WMSSA encounters a lack of convergence curve at about 700 iterations on function F7. However, this phenomenon is because the WMSSA converges to zero, so the curve cannot be displayed. Moreover, this phenomenon just proves that the WMSSA has a higher convergence accuracy.  
The convergence curves based on the comparison algorithms presented in Figure 7 are mainly used to highlight the superior performance of the proposed WMSSA. The mapping mutation operation introduces the SSA to expedite the convergence rate of the algorithm in the search process. Furthermore, the advantage of the dynamic weight can make the SSA require a better optimal solution. More importantly, the WMSSA shows better  
Figure 7: Convergence curves based on comparison algorithms.  
performance due to the introduction of these two developments, including escape from the local optimum and higher convergence accuracy.