# Multi-Strategy Hybrid Whale Optimization Algorithm Improvement
## 7 Conclusions and Future Work  
In conclusion, this paper uses three strategies to improve WOA. Experiments show that this improvement accelerates the optimization speed of WOA and improves its calculation accuracy. The convergence speed is also greatly improved. To be specific, it improves the algorithm's solution accuracy through parameter modifications and the introduction of decision parameters. Secondly, it enhances the algorithm's exploitation and exploration  
Figure 26: The contrast factor radar chart. Figure 25: Visual comparison between MAE and MAPE. capabilities through a dispersal search strategy. Thirdly, it incorporates simulated annealing to further improve the algorithm's ability to escape local optima. Through thirty independent tests comparing different types of algorithms, the experimental results demonstrate that MHWOA exhibits superior optimization capabilities and stability in solving global optimization problems, particularly in addressing complex problems. In terms of computation time, MHWOA shows significant improvements compared with WOA. In all tests, the calculation accuracy of MHWOA is improved by no less than 1.96%, the calculation error is reduced by no less than 1.83%, and the execution time is improved by no less than 5.6%. In the application of engineering problems, the calculation accuracy of MHWOA is improved by no less than 0.6%. In three real-world application scenarios, MHWOA consistently outperforms other algorithms in terms of computational accuracy. Additionally, MHWOA achieves substantial cost savings in practical problem-solving compared with other algorithms. These results highlight the superiority of MHWOA in solving practical engineering problems. When solving more complex CEC2022 problems, it can be found that MHWOA is more suitable for solving high-dimensional unimodal functions, basic functions, and composition functions with N greater than or equal to 5 according to the test results. MHWOA is weak in solving hybrid functions in medium and high dimensions, and although MHWOA can solve the optimal value in most problems, it is not its strong point. In order to further test the optimization performance of MHWOA in practical application scenarios, we respectively used standard WOA and MHWOA to optimize convolutional neural long- and short-term multi-head networks. The test results are analyzed from five dimensions. The results show that compared with the standard WOA, MHWOA reduces MAE by 24.71%, MAPE by 26.13%, MSE by 42.58%, RMSE by 24.23%, and R2 by 3.24% in the optimization process. After testing, the errors of MHWOA in all aspects of the prediction process are smaller than that of the standard WOA, and the degree of fitting with the true value is higher than that of the standard WOA. In the future, to address the challenges posed by the increased number of parameters and the complexity of the algorithm structure in MHWOA, we intend to explore further integration with other metaheuristic algorithms and simplify the computational process as much as possible. Through the above verification, although MHWOA performs well in solving most complex problems, it has a slow convergence speed in solving simple problems, so this improvement is more suitable for solving complex problems. Further improvements in the convergence rate for simple problems will be explored in the future. Conceptualization, X.X. and Y.Y. ; methodology, Y.Y. ; software, Y.Y. and H.Z. ; validation, X.X., Y.Y. and H.Z. ; formal analysis, Y.Y. ; investigation, X.X. and Y.Y. ; resources, X.X. ; data curation, X.X. and Y.Y; writing--original draft preparation, X.X. and Y.Y. ; writing--review and editing, X.X., Y.Y. and H.Z. ; visualization, Y.Y. ; supervision, X.X. ; project administration, X.X. ; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No.