Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Chou, Jui‐Sheng; Molla, Asmare

doi:10.1038/s41598-022-23121-z

Cited by 38 publications

(17 citation statements)

References 108 publications

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“…The hyperparameters are initialized to calculate the fitness values to find the current best food and its new location [29]. Overfitting is also prevented by monitoring the performance of the algorithm and fine tune the model to deploy the proposed method to classify the tomato plant leaf diseases [30]. The neural network is filled with training data and jellyfish population is also initialized.…”

Section: Novel Jf-resnet For Hyperparameter Optimizationmentioning

confidence: 99%

An intelligent deep residual learning framework for tomato plant leaf disease classification

Ezhilarasan,

Rani Ranganathan,

Shri Mani

et al. 2024

IJECE

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Modern agriculture has been fascinated by various advancements in agriculture and the processing of foods and supply management that provision farmers to improve production. The health of plants is essential to improve production and economic growth. Diseases in plants can affect production and create a rigorous impact on the quality and create a hazard to food safety. Hence, detecting and classifying plant leaf diseases is essential to prevent the disease spread across the plants in the agriculture field and to improve productivity. The researchers in existing frameworks utilized artificial intelligence and machine learning techniques to demonstrate noteworthy solutions. However, a few issues exist related to noises in the images, hyperparameter selection problems, and over-fitting problems that influence prediction accuracy. The proposed model jellyfish ResNet(JF-ResNet) works well to achieve a better accuracy level by incorporating jellyfish optimized ResNET for tomato plant leaf disease identification and classification. The performance metrics such as Accuracy, specificity, sensitivity, and F1-Score is used to evaluate the performance of the JF-ResNet model. The proposed model achieves 97.3% accuracy, 95.3% sensitivity, 96.1% specificity, 96.9% recall, 96.4% precision and 97.1% F1-Score.

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Section: Novel Jf-resnet For Hyperparameter Optimizationmentioning

confidence: 99%

An intelligent deep residual learning framework for tomato plant leaf disease classification

Ezhilarasan,

Rani Ranganathan,

Shri Mani

et al. 2024

IJECE

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“…To successfully explore the issue space and exploit potential regions for the best solutions, this balance is essential. These characteristics are what make JSO a desirable option for optimization jobs [29]. The fundamental concept behind JSO was inspired by the ways in which jellyfish behave in order to survive in the ocean.…”

Section: Jellyfish Search Optimizermentioning

confidence: 99%

Increasing Output Power of a Microfluidic Fuel Cell Using Fuzzy Modeling and Jellyfish Search Optimization

2023

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An efficient electrochemical energy conversion system with little to no environmental impact is the fuel cell (FC). FCs have demonstrated encouraging results in various applications and can even run on biofuel, such as bio-glycerol, a by-product of biodiesel. The most effective ways to operate FCs can significantly enhance their effectiveness. Incorporating fuzzy modeling and metaheuristic methods, this work used artificial intelligence to determine the ideal operating parameters for a microfluidic fuel cell (MFC). The concentrations of the following four variables were considered: bio-glycerol concentration, anode electrocatalyst loading, anode electrolyte concentration, and cathode electrolyte concentration. The output power density of the MFC was used to assess its performance. The output power density of the MFC was modeled using fuzzy logic, taking into account the aforementioned operational parameters. A jellyfish search optimizer (JSO) was then used to find the ideal operating conditions. The results were contrasted with response surface methodology (RSM) and experimental datasets to demonstrate the superiority of the proposed integration between fuzzy modeling and the JSO. In comparison with the measured and RSM approaches, the suggested strategy boosted the power density of the MFC by 9.38% and 8.6%, respectively.

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“…Metaheuristics methods have gained popularity over traditional methods in solving optimization problems due to their ease of use and the reliability of their outcomes. In recent times, swarm-based bio-inspired algorithms have been shown to be highly effective in solving a diverse array of optimization problems [32]. This paper utilized a recently developed swarm-based optimization strategy, which is called the "jellyfish search optimizer" (JSO).…”

mentioning

confidence: 99%

“…This algorithm is known for its high rate of convergence and its ability to effectively tune the controller parameters [33]. The JSO has been already used to solve a variety of problems [32] related to power system and energy generation, civil engineering, communication and networking, etc. However, it has not been applied to LFC problems until recently.…”

mentioning

confidence: 99%

Optimal Load Frequency Control of a Hybrid Electric Shipboard Microgrid Using Jellyfish Search Optimization Algorithm

Karnavas

Nivolianiti

2023

Applied Sciences

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This paper examines the critical topic of load frequency control (LFC) in shipboard microgrids (SMGs), which face challenges due to low system inertia and the intermittent power injection of renewable energy sources. To maintain a constant frequency (even under system uncertainties), a robust and well-tuned controller is required. In this paper, a study was conducted first by examining the performance of three different controller architectures, in order to determine which is the most-appropriate for the multi-energy SMG system. The time delays that occur due to communication links between the sensors and the controller were also considered in the analysis. The controllers were tuned using a very recent bio-inspired optimization algorithm called the jellyfish search optimizer (JSO), which has not been used until recently in LFC problems. To assess the tuning efficiency of the proposed optimization algorithm, the SMG’s frequency response results were comprehensively compared to the results obtained with other bio-inspired optimization algorithms. The results showed that the controllers with gains provided by the JSO outperformed those tuned with other bio-inspired optimization counterparts, with improvements in performance ranging from 19.13% to 93.49%. Furthermore, the robustness of the selected controller was evaluated under various SMG operational scenarios. The obtained results clearly demonstrated that the controller’s gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation.

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Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Cited by 38 publications

References 108 publications

An intelligent deep residual learning framework for tomato plant leaf disease classification

An intelligent deep residual learning framework for tomato plant leaf disease classification

Increasing Output Power of a Microfluidic Fuel Cell Using Fuzzy Modeling and Jellyfish Search Optimization

Optimal Load Frequency Control of a Hybrid Electric Shipboard Microgrid Using Jellyfish Search Optimization Algorithm

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