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Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore, predicting the compressive strength can facilitate the early planning of material quality manageme...
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... After pre-processing the leaf images, ResNet model is initialized with blocks and filters to train them using jellyfish algorithm with loss function as depicted in Algorithm 1. 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]. ...
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.
... The initializing of the individuals in JSA is performed using the information on a logical diagram [44], which removes the adverse impacts of randomly generated initial values commonly approved by conventional metaheuristics, such as a minimal convergence speed and a local optimum that can present a fall hazard as a consequence of an absence of the jellyfish variety. The following is an expression of the JSA-based rational diagram [43]: ...
One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, the production of oversize boulders creates many problems for the continuation of the work and usually imposes additional costs on the project. In this way, the breakage of oversize boulders is associated with throwing small fragments particles at high speed, which can lead to serious risks to human resources and infrastructures. Hence, the accurate prediction of flyrock induced by boulder blasting is crucial to avoid possible consequences and its’ environmental side effects. This study attempts to develop an optimized artificial neural network (ANN) by particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to construct the hybrid models for anticipating flyrock distance resulting in boulder blasting in a quarry mine. The PSO and JSA algorithms were used to determine the optimum values of neurons’ weight and biases connected to neurons. In this regard, a database involving 65 monitored boulders blasting for recording flyrock distance was collected that comprises six influential parameters on flyrock distance, i.e., hole depth, burden, hole angle, charge weight, stemming, and powder factor and one target parameter, i.e., flyrock distance. The ten various models of ANN, PSO–ANN, and JSA–ANN were established for estimating flyrock distance, and their results were investigated by applying three evaluation indices of coefficient of determination (R2), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R2, values of (0.957, 0.972 and 0.995) and (0.945, 0.954 and 0.989) were determined to train and test of proposed predictive models, respectively. The yielded results denoted that although ANN model is capable of anticipating flyrock distance, the hybrid PSO–ANN and JSA–ANN models can anticipate flyrock distance with more accuracy. Furthermore, the performance and accuracy level of the JSA–ANN predictive model can estimate better compared to ANN and PSO–ANN models. Therefore, the JSA–ANN model is identified as the superior predictive model in estimating flyrock distance induced from boulder blasting. In the final, a sensitivity analysis was conducted to determine the most influential parameters in flyrock distance, and the results showed that charge weight, powder factor, and hole angle have a high impact on flyrock changes.
... The results show that compared with traditional neural network models, the prediction model based on deep learning has the advantages of high accuracy, high efficiency, and strong generalization ability. Chou et al. [16] proposed a convolutional neural network based on computer vision for predicting the compressive strength of ready-mixed concrete. The results show that the neural network based on computer vision is superior to the neural network based on numerical data in all evaluation indicators. ...
In order to overcome the discreteness of input data and training data in deep neural network (DNN), the multivariable response surface function was used to revise input data and training data in this paper. The loss function based on the data on the response surface was derived, DNN based on multivariable response surface function (MRSF-DNN) was established. MRSF-DNN model of recycled brick aggregate concrete compressive strength was established, in which coarse aggregate volume content, fine aggregate volume content and water cement ratio are influencing factors. Furthermore, the predictive analysis and extended analysis of MRSF-DNN model were carried out. The results show that: MRSF-DNN model had high prediction accuracy, the correlation coefficient between the real values and the forecast values was 0.9882, the relative error was between -0.5% and 1%. Furthermore, MRSF-DNN had more stable prediction ability and stronger generalization ability than DNN.
... Solving the benchmark mathematical functions confirmed that the JS optimizer performed better than these algorithms. JSO has therefore been used to solve a diversity of engineering challenges [38][39][40]. According to the above discussion, the controller improvements resulted from the use of a modified FOPID controller, correct fractional computation, and a powerful optimizer for its parametric tuning. ...
... The direction of movement and current place of a jellyfish are modeled by Equations (14)- (16). This organization is deemed to be a successful exploitation of the regional quest space [40]. ...
... The timing control function varies at random intervals from zero to one. The time-varying control function is given by Equation (17) [40]. where c(t) is the timing function; c 0 is an initialized constant of 0.5; t is the timing given by the iteration number; and Max it is the initialized parameter specifying the maximum number of iterations. ...
In this article, a fractional-order proportional-integral-differential (FOPID) controller and its modified structure, called a MFOPID controller, are presented. To guarantee optimal system performance, the gains of the proposed FOPID and MFOPID controllers are well-tuned, employing the Jellyfish Search Optimizer (JSO), a novel and highly effective bioinspired metaheuristic approach. The proposed controllers are assessed in a hybrid system with two domains, where each domain contains a hybrid of conventional (gas, reheat, and hydro) and renewable generation sources (solar and wind). For a more realistic analysis, the presented system model includes practical limitations with nonlinear characteristics, such as governor dead zone/band (GDZ/GDB), boiler dynamics, generation rate limitation/constraint (GRL/GRC), system uncertainties, communication time delay (CTD), and load changes. The suggested methodology outperforms some newly developed heuristic techniques, including fitness-dependent optimizer (FDO), sine-cosine algorithm (SCA), and firefly algorithm (FA), for the interconnected power system (PS) of two regions with multiple generating units. Furthermore, the proposed MFOPID controller is compared with JSO-tuned PID/FOPID and PI controllers to ascertain its superiority. The results signify that the presented control method and its parametric optimization significantly outperforms the other control strategies with respect to minimum undershoot and peak overshoot, settling times, and ITSE in the system’s dynamic response. The sensitivity analysis outcomes imply that the proposed JSO-MFOPID control method is very reliable and can effectively stabilize the load frequency and interconnection line in a multi-area network with interconnected PS.
... Chou et al. 85 used JSO and convolutional neural networks (CNNs) to predict the compressive strength of ready-mixed concrete. Their analytical results reveal that computer vision-based CNNs outperform numerical data-based deep neural networks (DNNs). ...
The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.
