| 1. |
- Ramos-Michel, A., et al.
(author)
-
Improving Metaheuristic Algorithm Design Through Inequality and Diversity Analysis : A Novel Multi-Population Differential Evolution
- 2023
-
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). - : IEEE. - 9781665430654 ; , s. 1547-1552
-
Conference paper (peer-reviewed)abstract
- In evolutionary algorithms and metaheuristics, defining when applying a specific operator is important. Besides, in complex optimization problems, multiple populations can be used to explore the search space simultaneously. However, one of the main problems is extracting information from the populations and using it to evolve the solutions. This article presents the inequality-based multi-population differential evo-lution (IMDE). This algorithm uses the K-means to generate subpopulations (settlements). Two variables are extracted from the settlements, the diversity and the Gini index, which measure the solutions' distribution and the solutions' inequality regarding fitness. The Gini index and the diversity are used in the IMDE to dynamically modify the scalation factor and the crossover rate. Experiments over a set of benchmark functions with different degrees of complexity validate the performance of the IMDE. Besides comparisons, statistical and ranking average validate the search capabilities of the IMDE.
|
|
| 2. |
- Casas-Ordaz, A., et al.
(author)
-
Enhancing image thresholding segmentation with a novel hybrid battle royale optimization algorithm
- 2024
-
In: Multimedia tools and applications. - : Springer Nature. - 1380-7501 .- 1573-7721.
-
Journal article (peer-reviewed)abstract
- Accurate image segmentation is crucial in digital image processing, enabling efficient image analysis and robust vision systems. However, segmentation is a complex task as images vary in their characteristics, and the computational costs increase with the number of classes involved. To address these challenges, incorporating metaheuristic algorithms to guide the segmentation process presents an exciting opportunity for improvement. This research paper introduces a novel multilevel image segmentation approach that leverages a hybrid battle royale optimization algorithm. By combining opposition-based learning, highly disruptive polynomial mutation, differential evolution mutation, and crossover operators, the proposed method enhances the original battle royale optimization algorithm and effectively solves the segmentation problem. To evaluate the effectiveness of the proposed approach, the minimum cross-entropy criterion is applied to two sets of reference images that undergo multilevel thresholding with up to five thresholds. The results are compared with those obtained using nine other metaheuristic algorithms, employing various image quality metrics such as peak signal noise ratio, structural similarity index method, feature similarity index method, quality index based on local variance, Haar wavelet-based perceptual similarity index, and universal image quality index. The results are analyzed quantitatively, qualitatively, and statistically. The findings demonstrate the potential of the proposed approach in achieving high-quality multilevel thresholding image segmentation. Additionally, the hybrid battle royale optimization algorithm showcases its robustness and efficiency when compared to the other metaheuristic algorithms tested. Notable results are PSNR = 2.13E+01, SSIM = 8.41E-01, FSIM = 8.42E-01, QILV = 8.94E-01, HPSI = 6.52E-01, and UIQI = 9.78E-01.
|
|
| 3. |
- Morales-Castañeda, Bernardo, et al.
(author)
-
A Novel Diversity-Aware Inertia Weight and Velocity Control for Particle Swarm Optimization
- 2023
-
In: 2023 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9798350314588 - 9798350314588
-
Conference paper (peer-reviewed)abstract
- Particle Swarm Optimization (PSO) has efficiently solved several real-world applications and optimization problems. However, it has shortcomings, such as premature convergence and stagnation at local minima. Inertia weight is a parameter of this algorithm that controls the global and local exploration and exploitation capability by determining the influence of the previous velocity on its current motion. Therefore, this article proposes a PSO with a Diversity-aware Inertia and Velocity Control (PSOIVC) algorithm to improve the PSO performance. The PSOIVC employs a novel diversity-aware inertia weight and velocity control approach to tune the parameters to produce a trade-off between exploration and exploitation of the algorithm using the dimension-wise diversity. The PSOIVC algorithm is compared with eight algorithms, including variants of the PSO, on a set of 30 benchmark functions for a single objective real parameter in 30 and 50 dimensions. Based on the results, the proposal presents significant outcomes according to the average values obtained for both comparisons; because it performed similarly or better than the other algorithms in 23/30 and 16/30 for 30 and 50 dimensions, respectively.
|
|
| 4. |
- Morales-Castañeda, Bernardo, et al.
(author)
-
Improving the Convergence of the PSO Algorithm with a Stagnation Variable and Fuzzy Logic
- 2023
-
In: 2023 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9798350314588 - 9798350314588
-
Conference paper (peer-reviewed)abstract
- Particle swarm optimization (PSO) is essential to evolutionary computation algorithms (ECA). The PSO has some drawbacks as premature convergence and stagnation at local minima. Inertia weight is a parameter that controls the global and local exploration and exploitation capability in the PSO by determining the influence of the previous velocity on its current motion. This article proposes using a stagnation counter that verifies the times the PSO is stuck in the same fitness value. In the proposed fuzzy controlled PSO with stagnation coefficient (FCPSO), a fuzzy controller is designed to tune the inertia weight based on the population's diversity and the search's stagnation. This modification allows the PSO to escape from suboptimal values enhancing its search capabilities. The FCPSO is tested over 28 benchmark functions in 50 dimensions. Besides, it has been compared with nine optimization algorithms from the state-of-the-art. The experiments and comparisons suggest that the FCPSO is an interesting tool for solving complex optimization problems.
|
|
