Water Wave Optimization Research Articles

Enhancing disaster management through multi-objective water wave optimization for medical supplies storage and distribution

This paper conducts a comparative analysis of advanced methodologies aimed at addressing the intricate task of scheduling medical supplies in both civilian and military sectors for epidemic prevention and control. This study introduces a multi-objective water wave optimization (MOWWO) algorithm and enhance its efficacy by incorporating a dynamically adjusted component to the metaheuristic approach (DAMOWWO). The primary goal of this research is to assess the proposed approach in contrast to established state of the art methods with similar objectives. The aim of this study is to optimize multiple aspects simultaneously, including the overall satisfaction rates of medical supply delivery and the reduction of scheduling costs, while ensuring a minimum military supply reservation ratio. This paper offers a comprehensive evaluation of the MOOW algorithm, emphasizing its potential applications in emergency response scenarios.

Extended water wave optimization (EWWO) technique: a proposed approach for task scheduling in IoMT and healthcare applications

AbstractThe Internet of Medical Things (IoMT) is a version of the Internet of Things. It is getting the attention of researchers because it can be used in a wide range of smart healthcare systems. One of the main advancements employed recently is the IoMT-cloud, which allows users to access cloud services remotely over the internet. These cloud services require an efficient task scheduling approach that satisfies the Quality of Service parameters with a low energy consumption. This paper presents an overview of the integration of IoMT and cloud computing technologies. Besides,this work proposes an efficient Extended Water Wave Optimization (EWWO) task scheduling in the IoMT Cloud for healthcare applications. EWWO algorithm performs based on its operations propagation, refraction and breaking. The proposed EWWO scheduling technique minimizes the energy consumption, makespan time, execution time and increases the resource utilization. Cloudsim simulator is used to simulate the IoMT-Cloud environment to verify the effectiveness of EWWO technique. The performance has been evaluated based on various parameters such as energy consumption, makespan time and execution time.

CNNFDNF: CONVOLUTIONAL NEURAL NETWORK AND CASCADE NEURO FUZZY NETWORK FOR SEVERITY LEVEL OF COVID-19 DETECTION USING CHEST X-RAY IMAGES

Chest X-ray (CXR) images are the most important imaging modality to monitor and detect several lung diseases and Coronavirus Disease 2019 (COVID-19) disease. This is the first imaging method in the detection of COVID-19. Since the limited availability of interpreted medical images, and the accuracy are the biggest challenges in the conventional methods. Hence, a novel framework is designed for the severity level of COVID-19 detection using CXR images named Convolutional Neural Network and Cascade Neuro-Fuzzy Network (CNNFDNF). Initially, the input CXR image is allowed to the preprocessing phase that is done utilizing the Kalman filter and Region of Interest (ROI) extraction. Then, the lung lobe segmentation is executed by Psi-Net. After that, the extraction of Convolutional Neural Network (CNN) features is conducted from the interested lobe region. Moreover, the extracted image features are subjected to COVID-19 detection, where it is achieved by Deep Generative Adversarial Network (Deep GAN) trained by Henry Gas Water Wave Optimization (HGWWO). Here, HGWWO is blended by Water Wave Optimization (WWO) and Henry Gas Solubility Optimization (HGSO). The detection phase is categorized into COVID and non-COVID. If the detected image is COVID, then the CXR images are given to the second level classification. Finally, it is accomplished by employing the proposed CNNFDNF to classify them into low, moderate, or high COVID-19 infection affected at lungs. The proposed CNNFDNF is the newly devised scheme by modifying the layers of CNN and cascaded neuro-fuzzy network (DNF) structure. The measures employed for this scheme namely, accuracy, sensitivity and specificity acquired are 93.0%, 94.1%, and 93.8%. The proposed method is essential for keeping the situation under control. Because, in the severe cases may have severe pneumonia, other organ failure, and possible death.

Enhancing Water treatment predictions: a Machine Learning Approach with CNN and Water Wave optimization

Enhancing Water treatment predictions: a Machine Learning Approach with CNN and Water Wave optimization

Research on Data-Driven Fault Scenario Simulation Model of Distributed Network

Distribution lines are critical components of today's power system, and can directly impact on power supply security and stability. An effective power system protection system should detect any faults as soon as they occur. There are two steps in fault diagnosis. One is defect classification, which has already achieved excellent accuracy rates. As a result, this study concentrates on the opposite objective, which is fault location. This research introduces a War Strategy Water Wave Optimization (WSWWO) based Deep Q Network for radial distribution networks using synchronous generators for Distributed Generation (DG). The algorithm estimates the fault site by analyzing voltage and current samples at the main feeder head, and scheduled active and reactive power injections by network synchronous generators. A full-order synchronous machine model was utilized to analyze the dynamic behavior of DG plants during fault transients. Here, the Deep Q Network is trained by WSWWO, and hybridized by two optimization algorithms, War Strategy Optimization (WSO) and Water Wave Optimization (WWO). Furthermore, the experimental results revealed that the WSWWO-Deep Q Network outperformed state-of-the-art models in terms of Accuracy and Error, with values of 0.997 and 0.141, respectively.

Intelligent Differentiation Framework for Lewy Body Dementia and Alzheimer’s disease using Adaptive Multi-Cascaded ResNet–Autoencoder–LSTM Network

In recent years, most of the patients with dementia have acquired healthcare systems within the primary care system and they also have some challenging psychiatric and medical issues. Here, dementia-based symptoms are not identified in the primary care center, because they are affected by various factors like psychological symptoms, clinically relevant behavior, numerous psychotropic medications, and multiple chronic medical conditions. To enhance the healthcare-related applications, the primary healthcare system with additional resources like coordination with interdisciplinary dementia specialists, feasible diagnosis, and screening process need to be improved. Therefore, the differentiation between Alzheimer’s Disease (AD) and Lewy Body Dementia (LBD) has been acquired to provide the best clinical support to the patients. In this research work, the deep structure depending on AD and LBD systems has been implemented with the help of an adaptive algorithm to provide promising outcomes over dementia detection. Initially, the input images are collected from online sources. Thus, the collected images are forwarded to the newly designed Multi-Cascaded Deep Learning (MSDL), where the ResNet, Autoencoder, and weighted Long-Short Term Memory (LSTM) networks are serially cascaded to provide effective classification results. Then, the fully connected layer of ResNet is given to the Autoencoder structure. Here, the output from the encoder phase is optimized by using the Adaptive Water Wave Cuttlefish Optimization (AWWCO), which is derived from the Water Wave Optimization (WWO) and Cuttlefish Algorithm (CA), and the resultant selected output is fed to the weight-optimized LSTM network. Further, the parameters in the MSDL network are optimized by using the same AWWCO algorithm. Finally, the performance comparison over different heuristic algorithms and conventional dementia detection approaches is done for the validation of the overall effectiveness of the suggested model in terms of various estimation measures.

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes.

Feature level fusion for land cover classification with landsat images: A hybrid classification model

Classification of land cover using satellite images was a major area for the past few years. A raise in the quantity of data obtained by satellite image systems insists on the requirement for an automated tool for classification. Satellite images demonstrate temporal or/and spatial dependencies, where the traditional artificial intelligence approaches do not succeed to execute well. Hence, the suggested approach utilizes a brand-new framework for classifying land cover Histogram Linearisation is first carried out throughout pre-processing. The features are then retrieved, including spectral and spatial features. Additionally, the generated features are merged throughout the feature fusion process. Finally, at the classification phase, an optimized Long Short-Term Memory (LSTM) and Deep Belief Network (DBN) are introduced that portrays classified results in a precise way. Especially, the Opposition Behavior Learning based Water Wave Optimization (OBL-WWO) model is used for tuning the weights of LSTM and DBN. Atlast, many metrics illustrate the new approach’s effectiveness.

Performance evaluation and comparative analysis of CrowWhale-energy and trust aware multicast routing algorithm

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, CrowWhale-energy and trust-aware multicast routing (CrowWhale-ETR) have been devised. In this research, the routing performance of CrowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the Spider Monkey Optimization algorithm (SMO), Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, Crow Search Algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing Algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of CrowWhale-ETR. In addition, the performance of CrowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

Deep Learning-Based Wildfire Image Detection and Classification Systems for Controlling Biomass

Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence-based forest fire warning system to prevent major disasters. This work aims to present an overview of vision-based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception-v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision-makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN-RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no-fire photos, the proposed RBFN-RAISR fire detection approach achieves 97.55% accuracy, 93.33% F-Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one-of-a-kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.

Optimization assisted autoregressive technique with deep convolution neural network-based entropy filter for image demosaicing

ABSTRACT This paper presents an image demosaicing based on an optimization-driven deep learning model, namely the Autoregressive Water Wave Optimization algorithm (Autoregressive-WWO). The proposed method is devised by assimilating the Wave Optimization algorithm (WWO), and the Conditional autoregressive value at risk (CAViaR) model. Here, the input images are subjected to Autoregressive WWO-based local polynomial approximation and intersection of confidence intervals (LPA-ICI) filter, and Deep Convolution neural network (Deep CNN) in a concurrent manner. The filter coefficients are obtained from the proposed Autoregressive WWO-based LPA-ICI filter and the residual image is obtained from Deep CNN. In order to create the demosaiced image, these two outputs are combined using an entropy measure. The proposed method offered superior performance with the highest Peak signal to noise ratio (PSNR) of 40.049dB, the highest Second derivative measure of enhancement (SDME) of 50.168dB, and highest Structural Index Similarity (SSIM) of 0.9056.

Energy management and power quality improvement of microgrid system through modified water wave optimization

A comparative study of energy management strategies and PQ improvement schemes for a Fuel Cell, Battery, and SuperCapacitor integrated Microgrid system has been projected utilizing the MATLAB/Simulink architecture in this research article. For effective energy management and PQ enhancement, a Modified Water Wave Optimization algorithm based on adaptive population size and adaptive wavelength coefficient has been proposed. The MWWO method robustly and dynamically tunes the parameters of the Proportional Integral controller for effective operation. The efficiency of the proposed technique has been compared with the traditional methods regarding hydrogen consumption, load power deliberation, power quality and overall system efficiency. The results obtained and the numerical analysis confers the superiority of the suggested technique over other methods for enhanced dynamic voltage response, low fuel consumption, reduced harmonics and better efficacy thereby proving its real-time employment.

Swarm Exploration Mechanism-Based Distributed Water Wave Optimization

Using sparrow search hunting mechanism to improve water wave algorithm (WWOSSA), which combines the water wave optimization (WWO) algorithm and the sparrow search algorithm (SSA), has good optimization ability and fast convergence speed. However, it still suffers from insufficient exploration ability and is easy to fall into local optimum. In this study, we propose a new algorithm for distributed population structure, called swarm exploration mechanism-based distributed water wave optimization (DWSA). In DWSA, an information exchange component and an optimal individual evolution component are designed to improve information exchange between individuals. This multi-part information interaction and distributed population structure algorithm can help the population algorithm to establish a balance between exploitation and exploration more effectively. We contrast DWSA with the original algorithms WWOSSA and other meta-heuristics in order to show the effectiveness of DWSA. The test set consists of 22 actual optimization issues from the CEC2011 set and 29 benchmark functions from the CEC2017 benchmark functions. In addition, an experimental comparison of the parameter values introduced in DWSA is included. According to experimental results, the proposed DWSA performs substantially better than its competitors. Assessments of the population diversity and landscape search trajectory also confirmed DWSA’s outstanding convergence.

Hybridization of Energy Optimization Technique for Cluster Based Routing using Various Computational Intelligence Methods in WSN

Approaches in WSN technology has determined by opportunity of tiny and inexpensive sensor nodes with adequacy of sensing multiple kinds of information processing and wireless communication. Network lifetime and energy efficiency are major indexes of WSN. Several clustering techniques are intended to extend the network lifetime but whereas there is an issue of incompetent Cluster Head (CH) election. To overcome this issue, an Integration of Novel Memetic and Brain Storm Optimization approach with Levy Distribution (IoNM-BSOLyD) has been proposed for clustering using fitness function. In the meanwhile, election of CH is done by utilizing fitness function, which incorporates following amplitude such as energy, distance to adjacent nodes, distance to BS, and network load. After clustering, routing techniques decides the detecting and pursuing the route in WSN. In this proposed work, a Water Wave Optimization with Hill Climbing technique (WWO-HCg) is introduced for routing purpose. This proposed methodology deals with ternary QoS aspect such as network delay, energy consumption, packet delivery ratio, network lifetime and security to select optimal path and enhance QoS as well. This proposed protocol provides better performance result than other contemporary protocols.

Rider Water Wave-enabled deep learning for disease detection in rice plant

Rice diseases have degraded the production of the rice plant, which produces economic loss. To control and minimize the effects of attacks, the diseases are required to be recognized at a premature stage. Premature detection of infections can improve the yield from quantitative as well as qualitative losses, diminish the usage of pesticides, and improve the economic growth of the country. Hence, this paper devises a new method, namely Rider Water Wave-based neural network (RWW-NN) for finding the disease in the rice plant, where the training of NN is completed using the RWW, which is formed by assimilating Rider Optimization algorithm (ROA) and Water wave optimization (WWO). Initially, the pre-processing is done by using histogram equalization from the input image. Then, the segmentation is completed using Segmentation Network (SegNet), and then the CNN features are employed for feature mining in order to acquire the optimal features for disease recognition. These features are fed to NN for disease detection wherein the RWW is introduced for training the optimal weights. The RWW-based NN acquires greatest accuracy of 0.908, F-measure of 0.907, sensitivity of 0.862, and specificity of 0.947 based on K-value using Rice disease dataset.

OPTIMIZATION ASSISTED AUTOREGRESSIVE METHOD WITH DEEP CONVOLUTIONAL NEURAL NETWORK-BASED ENTROPY FILTER FOR IMAGE DEMOSAICING

The natural scenes are captured by digital cameras that adopt a single charged-coupled device (CCD) or Complementary Metal Oxide Semiconductor (CMOS) sensor with a Color Filter Array (CFA). To reconstruct a full color image from the mosaiced CFA image, the missing components of color are recovered by a technique called color demosaicing. This research work introduces Deep Convolution Neural Network (DCNN)-based optimization assisted autoregressive method for image demosaicing. Here, the proposed optimization algorithm, termed Adaptive Autoregressive Water Wave Optimization algorithm (Adaptive Autoregressive-WWO), that is formed by combining Conditional autoregressive value at risk (CAViaR) model and Water Wave Optimization algorithm (WWO). Fusion process is carried out for residual images to generate the final demosaiced image based on entropy measure. Here, the output generated from DCNN is the first residual image. The LPA-ICI filter utilizes the optimization algorithm that generated second order polynomial coefficients to produce the second residual output image. Moreover, this proposed method is evaluated for its performance using metrics, such as Peak signal-to-noise ratio (PSNR) and Second Derivative like Measurement (SDME) and attained the highest PSNR values of 40.379dB and highest SDME values of 50.675dB.

Multi-Objective Optimization Model and Hierarchical Attention Networks for Mutation Testing

Mutation testing is devised for measuring test suite adequacy by identifying the artificially induced faults in software. This paper presents a novel approach by considering multiobjectives-based optimization. Here, the optimal test suite generation is performed using the proposed water cycle water wave optimization (WCWWO). The best test suites are generated by satisfying the multi-objective factors, such as time of execution, test suite size, mutant score, and mutant reduction rate. The WCWWO is devised by a combination of the water cycle algorithm (WCA) and water wave optimization (WWO). The hierarchical attention network (HAN) is used for classifying the equivalent mutants by utilizing the MutPy tool. Furthermore, the performance of the developed WCWWO+HAN is evaluated in terms of three metrics—mutant score (MS), mutant reduction rate (MRR), and fitness—with the maximal MS of 0.585, higher MRR of 0.397, and maximum fitness of 0.652.

Crop cultivation planning with fuzzy estimation using water wave optimization.

In a complex agricultural region, determine the appropriate crop for each plot of land to maximize the expected total profit is the key problem in cultivation management. However, many factors such as cost, yield, and selling price are typically uncertain, which causes an exact programming method impractical. In this paper, we present a problem of crop cultivation planning, where the uncertain factors are estimated as fuzzy parameters. We adapt an efficient evolutionary algorithm, water wave optimization (WWO), to solve this problem, where each solution is evaluated based on three metrics including the expected, optimistic and pessimistic values, the combination of which enables the algorithm to search credible solutions under uncertain conditions. Test results on a set of agricultural regions in East China showed that the solutions of our fuzzy optimization approach obtained significantly higher profits than those of non-fuzzy optimization methods based on only the expected values.

Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight

Association rule mining (ARM) is one of the most important tasks in data mining. In recent years, swarm intelligence algorithms have been effectively applied to ARM, and the main challenge has been to achieve a balance between search efficiency and the quality of the mined rules. As a novel swarm intelligence algorithm, the water wave optimization (WWO) algorithm has been widely used for combinatorial optimization problems, with the disadvantage that it tends to fall into local optimum solutions and converges slowly. In this paper, a novel hybrid ARM method based on WWO with Levy flight (LWWO) is proposed. The proposed method improves the solution of WWO by expanding the search space through Levy flight while effectively increasing the search speed. In addition, this paper employs the hybrid strategy to enhance the diversity of the population in order to obtain the global optimal solution. Moreover, the proposed ARM method does not generate frequent items, unlike traditional algorithms (e.g., Apriori), thus reducing the computational overhead and saving memory space, which increases its applicability in real-world business cases. Experiment results show that the performance of the proposed hybrid algorithms is significantly better than that of the WWO and LWWO in terms of quality and number of mined rules.