For example, in [52], the OBL strategy is used to increase the search efficiency of the HHO algorithm. Further details on the stages of the proposed image thresholding model are provided below. Like many other metaheuristic algorithms, MPA-OBL starts by randomly initializing the first population x0 and storing the results.
After applying three stages of optimization, the proposed algorithm calculates and compares the suitability of-→. Therefore, the total time complexity of the proposed MPA-OBL can be represented by O(tmax×T×N×D). This section introduces the evaluation process of the new version of MAP called MPA-OBL.
As mentioned above, the CEC’2020 benchmark functions are used to evaluate the performance of the proposed MPA-OBL, including quantitative and qualitative metrics. The results in terms of mean and STD reveal the superiority of the proposed algorithm in solving the eight benchmark functions of CEC’2020 compared to other competing algorithms. As shown, the proposed MPA-OBL achieves average and best optimal solutions for most test functions compared to other methods.
The plots of the proposed MPA-OBL algorithm are, for most functions, very narrow compared to other algorithm distributions, and therefore with the lowest values. This subsection presents a convergence analysis of the proposed MPA-OBL compared to the other competitor algorithms. Moreover, the proposed algorithm achieves the lowest average of the best solutions so far the fastest for most functions.
Furthermore, the analysis of the results obtained from the proposed algorithm in terms of suitability, PSNR, FSIM, and. This section presents and discusses the numerical results of the results of the proposed MPA-OBL algorithm, where the variance between Otsu's classes, Eq. 17) is used as an objective function. From the PSNR results shown in Table 11 in terms of the average values associated with PSNR, it is observed that the proposed MPA-OBL ranks first with 13 higher cases, while CMA_ES-OBL comes in second with 12 higher cases, SCA-OBL. and MPA algorithms ranked third with 11 higher cases.
When applying the rank sum test between the proposed MPA-OBL algorithm and each of the following algorithms (LSHADE_SPACMA-OBL, CMA_ES-OBL, DE-OBL, HHO-OBL, SCA-OBL, SSA-OBL and the original MPA) it is observed that there is a change of LSHADE_. Overall, in both Otsu's and Kapur's objective functions, the segmentation effect of MPA-OBL represents the dominance of the proposed MPA-OBL compared to other algorithms. This section mentions some of the advantages and disadvantages related to the proposed MPA-OBL method.
The main disadvantages of the proposed algorithm are that MPA-OBL uses only the intensity information of the image to perform segmentation tasks which leads to the sensitivity to noise.
Conclusion
Test image nTh LSHADE_SPACMA-OBL CMA_ES-OBL DE-OBL HHO-OBL SCA-OBL SSA-OBL MPA MPA-OBL. The experimental dataset is not efficient enough to measure the performance of the segmentation process. Results in terms of standard deviation are not good enough to compete with the other algorithms.
Test image nTh LSHADE_SPACMA-OBL CMA-OBL DE-OBL HHO-OBL SCA-OBL SSA-OBL MPA MPA-OBL. Mean STD Mean STD Mean STD Mean STD Mean STD Mean STD Mean STD Mean STD. The average of the time values for 30 runs was obtained with different algorithms for the segmentation experiment based on Kapur's objective function.
Medium Std Medium Std Medium Std Medium Std Medium Std Medium Std Medium Std Medium Std Comparison of P and H values obtained from the Wilcoxon signed-rank test between the pairs of the proposed MPA-OBL vs LSHADE_SPACMA-OBL, MPA-OBL vs CMA_ES-OBL, MPA-OBL vs DE-OBL, MPA-OBL vs HHO- OBL, MPA-OBL vs SCA-OBL, MPA-OBL vs SSA-OBL and MPA-OBL vs MPA for Kapur's method in terms of fitness results. Test Image nTh LSHADE_SPACMA-OBL CMA_ES-OBL DE-OBL HHO-OBL SCA-OBL SSA-OBL MPA.
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