Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Akkar, Hanan A. R.; Salman, Sameem Abbas

doi:10.22266/ijies2020.1231.25

Cited by 3 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 5, the minimum reactive power loss value obtained by the MHBA at -24.16 MVAR is considered 80.94% lower than the initial case (-4.606 MVAR). Furthermore, this value is 1.66%, 15.81%, 10.76%, 16.60%, 12.42% lower than PSO [42], SA [66], DE [67], BHBO [47] and GWO [41] algorithms, respectively. On the other hand, the BHBO [39] showed the highest value at (-20.1522 MVAR).…”

Section: Resultsmentioning

confidence: 94%

“…To verify the effectiveness of the MHBA algorithm in solving the OPF problem, it has been compared with other optimization algorithms reported in the literature, namely, PSO [26,42,65], GWO [22,41], ABC [33], SCA [38], HHO [31], EGA [37], SA [66], DE [67], BHBO [47], GSA [28], BHBO [39], DSA [34], BBO [34] algorithms. In this comparison, the five objectives, fuel cost minimization, minimization of active and reactive power transmission line losses, minimization of VD, and enhancement of the voltage stability have been considered.…”

Section: Resultsmentioning

confidence: 99%

“…According to [45], the HBA is superior to other metaheuristics proposed in the literature, among them, PSO [49], MFO [50], WOA [51], GOA [52] and HHO [10]. In solving real-world problems [45], the HBA has shown superior performance compared to the recently proposed metaheuristics [11,46,47]. However, the convergence analysis presented in [45] showed that the HBA has a drawback of trapping in local optima especially in solving a complex optimization problem.…”

Section: Introductionmentioning

confidence: 97%

“…Its advantage includes performing a dynamic search behaviour to find the food source, which helps in maintaining the trade-off balance between exploration and exploitation. Another advantage of HBA, it has a few parameters to adjust compared to other recently proposed algorithms, such as the African vultures optimization algorithm (AVOA) [46], HGSO [11], cicada swarm optimization [47] and Coronavirus herd immunity optimizer (CHIO) [48]. Furthermore, a sensitivity analysis for the control parameters is not available for these algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

2022

IJIES

View full text Add to dashboard Cite

This paper proposes a modified honey badger algorithm (MHBA) for solving the optimal power flow (OPF) problem. This problem is a highly non-linear, non-convex and complex optimization problem with several decision variables and constraints. The original honey badger algorithm (HBA) has the problem of trapping in local optima due to the loss of population diversity, especially in solving complex optimization problems. Therefore, the MHBA aims at sufficient improvement in finding the optimal solution and feasibility. Opposition-based learning strategy (OBL) is integrated with the MHBA to preserve the diversity of the population and enhance the convergence toward the optimal solution. The effectiveness of the MHBA algorithm is evaluated on five objective functions of the OPF problem namely, total generation fuel cost minimization, active power and reactive power transmission losses minimization, voltage deviation and voltage stability enhancement. The performance of the proposed algorithm is tested and validated on the IEEE 30-bus test system. The proposed MHBA is compared with the HBA and other nature-inspired optimization algorithms reported in the literature. The results indicate that the proposed MHBA algorithm has the superiority to jump out of the local optimal and better convergence in solving the OPF problem. This is due to the strategy used in the algorithm which helps in maintaining the population diversity and provides a proper balance between exploration and exploitation.

show abstract

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

2022

IJIES

View full text Add to dashboard Cite

show abstract

Efficient Optimization Algorithm for IoT-based Cognitive Radio Routing Model with Energy Harvesting for Medical Data Transmission

Kumar,

Singh

2023

2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management

View full text Add to dashboard Cite

Efficient Segmentation Model Using Mri Images and Deep Learning Techniques for Multiple Sclerosis Classification

Langat,

Zou,

Kui

et al. 2024

Int J Mult Comp Eng

View full text Add to dashboard Cite

The segmentation models employing deep learning offer successful outcomes over multiple medical image complex data resources and public data resources important for huge pathologies. During the identification of multiple sclerosis, the observation of entire tumors from the magnetic resonance imaging (MRI) sequence is complex. Furthermore, it is necessary to identify the small tumors from the images in the prognosis phase to offer good treatment. The deep learning-assisted identification models solve the issue of the imbalance data, and the false positive results are more in the conventional models. Besides, these methodologies offer a good tradeoff between the precision measure and recall measure. Thus, the latest deep learning-assisted MRI image segmentation and categorization model is developed to detect multiple sclerosis at the initial stage. In this study, the MRI images are initially gathered. The gathered images are directly given to the image segmentation process, where the Multi-Scale Adaptive TransResunet++ (MSAT) is adopted to perform the lesion segmentation appropriately. The attributes present in the MSAT are optimized with the support of the developed random opposition of cicada swarm optimization (ROCSO). Then, the segmented pictures are subjected to the categorization process where the hybrid and dilated convolution-based adaptive residual attention network (HDCARAN) is utilized. The HDCARAN categorizes the lesions from the MRI images very effectively and detects the multiple sclerosis of patients. Here, the attributes present within the HDCARAN are tuned via the same ROCSO. The implementation results are analyzed through the previously developed multiple sclerosis detection schemes to evaluate the effectiveness of the designed model with respect to several functionality measures. The implementation of HDCARAN results in the detection of lesions in the initial stages, with a 94% specificity level, as well as efficiency performance based on existing models. The model also obtains a 94.5% accuracy, as opposed to the other conventional systems. The results show the proposed method performs better than other machine learning models.

show abstract

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Cited by 3 publications

References 47 publications

A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

Efficient Optimization Algorithm for IoT-based Cognitive Radio Routing Model with Energy Harvesting for Medical Data Transmission

Efficient Segmentation Model Using Mri Images and Deep Learning Techniques for Multiple Sclerosis Classification

Contact Info

Product

Resources

About