Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

The Gaussian Mutational Barebone Dragonfly Algorithm: From Design to Analysis

The dragonfly algorithm is a swarm intelligence optimization algorithm based on simulating the swarming behavior of dragonfly individuals. An efficient algorithm must have a symmetry of information between the participating entities. An improved dragonfly algorithm is proposed in this paper to further improve the global searching ability and the convergence speed of DA. The improved DA is named GGBDA, which adds Gaussian mutation and Gaussian barebone on the basis of DA. Gaussian mutation can randomly update the individual positions to avoid the algorithm falling into a local optimal solution. Gaussian barebone can quicken the convergent speed and strengthen local exploitation capacities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of engineering design. To verify the superiorities of GGBDA, this paper sets 30 benchmark functions, which are taken from CEC2014 and 4 engineering design problems to compare GGBDA with other algorithms. The experimental result show that the Gaussian mutation and Gaussian barebone can effectively improve the performance of DA. The proposed GGBDA, similar to the DA, presents improvements in global optimization competence, search accuracy, and convergence performance.

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.