Water Wave Optimization Algorithm Research Articles

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.

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.

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.

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.

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.

Authentication-based multimodal biometric system using exponential water wave optimization algorithm

Authentication-based multimodal biometric system using exponential water wave optimization algorithm

Water wave optimized nonsubsampled shearlet transformation technique for multimodal medical image fusion

SummaryMedical image fusion has advanced to the point that it is now possible to combine multiple medical images for accurate disease diagnosis and treatment. The state‐of‐art techniques based on spatial and transform domains suffer from different limitations such as low fused image quality, spectral degradation, contrast reduction, low edge information preserving, lack of shift‐invariance, high computational complexity, classification accuracy, and sensitivity to noise. The main motivation of this work is to generate a single image with excellent visual clarity that retains the features of the source images. This article proposes a water wave optimized nonsubsampled shearlet transformation technique (NSST) for multimodal medical image fusion, in which the water wave optimization (WWO) algorithm is used to allocate the weights of the NSST approach's high‐frequency subbands. The NSST approach is primarily used in this work due to its ability to withstand shift‐invariance and its potential to improve the visual clarity of the fused multimodal image by preserving the essential features present in the image's various directions and edges. We combined the NSST technique with the WWO algorithm, which processes the edges, details, and contourlets of medical images using a max selection strategy based on the fitness function, to improve image quality and computational costs. The WWO algorithm is mainly applied to the NSST to minimize the L1 distance between the fused and the source images. Hence to overcome this problem a condition CNN optimized with a hybrid tunicate swarm memetic (TSM) algorithm is used to incorporate both the benefits offered by the condition CNN–TSM algorithm and NSST. The TSM optimized condition CNN architecture is used to preserve the coefficients of the image and improve the perceiving capability of the high‐frequency sub‐bands. An inverse NSST is used for fused frequency sub‐band integration. Finally, the efficiency of the proposed methodology is evaluated in terms of enhanced visual feature quality, edge detection, contour detection, and computational performance.

An Optimized Deep Learning Based Optimization Algorithm for the Detection of Colon Cancer Using Deep Recurrent Neural Networks

Colon cancer is the second leading dreadful disease-causing death. The challenge in the colon cancer detection is the accurate identification of the lesion at the early stage such that mortality and morbidity can be reduced. In this work, a colon cancer classification method is identified out using Dragonfly-based water wave optimization (DWWO) based deep recurrent neural network. Initially, the input cancer images subjected to carry a pre-processing, in which outer artifacts are removed. The pre-processed image is forwarded for segmentation then the images are converted into segments using Generative adversarial networks (GAN). The obtained segments are forwarded for attribute selection module, where the statistical features like mean, variance, kurtosis, entropy, and textual features, like LOOP features are effectively extracted. Finally, the colon cancer classification is solved by using the deep RNN, which is trained by the proposed Dragonfly-based water wave optimization algorithm. The proposed DWWO algorithm is developed by integrating the Dragonfly algorithm and water wave optimization.

A Knowledge-Based Adaptive Discrete Water Wave Optimization for Solving Cloud Workflow Scheduling

Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this paper investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

Competitive verse water wave optimisation enabled COVID-Net for COVID-19 detection from chest X-ray images

ABSTRACT Coronavirus disease 2019 (COVID-19) pandemic has a rigorous impact on the healthiness of the world’s population. Therefore, in order to stop the spread of the disease, it is crucial to identify COVID-19 as soon as feasible. Therefore, a robust and effective competitive verse water wave optimisation-based COVID-Net (CVWWO-based COVID-Net) technique is developed in the detection of COVID-19. The CVWWO is designed by incorporating the competitive multi-verse optimizer (CMVO) and water wave optimization (WWO) algorithm. The region of interest (RoI) extraction approach is used in the removal of falsifications from the input chest X-ray (CXR) images. Thereafter, the segmentation of lung lobe is determined using kernel-based Bayesian fuzzy clustering (BFC). The features are useful in the detection of COVID-19. Here, the COVID-Net is accomplished, in which the training is performed using the CVWWO. The best results of the implemented approach are total positive rate (TPR) of 96%, total negative rate (TNR) of 93.72% and accuracy of 96%.

Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm

Unpredictable nature of renewable energy sources puts power system operators in different conditions in terms of maintaining the system reliability. Microgrids (MGs) can provide an effective solution for the problems of grid-connected renewable energy sources due to their tuning capability and flexibility. In this study, a stochastic optimization strategy was proposed for participating in energy market operations considering demand response (DR). The results indicated the operating cost decreased when the MG implemented DR programs. Also, DR program could shift energy consumption from on- to off-peak hours and flatten the load curve. Therefore, a scheduling model was presented for the operation of energy carriers and reserves considering security constraints of power and natural gas grids in interconnected hubs as well as responsive load participation using the developed water wave optimization (WWO) algorithm. The objective function of this model aimed to minimize the operating cost of sources to supply electrical and thermal loads applied to the proposed MG. WWO is a meta-heuristic algorithm inspired by the behavior of water waves. The waves formed on the sea surface have complex, but interesting, relationships that can be used to solve optimization problems. In this algorithm, each problem solution is encoded as a water wave and undergoes changes in the problem search space based on three behaviors of propagation, refraction, and decay of water waves or problem solutions. The simplicity of the structure of this method, elitism and the ability to escape from local points due to the existence of different search operators and the mutation of generations have led to the use of this method. The results indicated a decline in operating costs through electrical and thermal responsive load participation and thermal energy storage system. Results of the proposed model revealed a correlation between the electricity price and natural gas consumption, indicating multi-carrier energy grids should be examined and optimized simultaneously.

A bicriterion single-machine scheduling problem with step-improving processing times

Time-dependent scheduling problems, where the real processing time of jobs is dependent on the starting time, have received growing attention in recent decades. In particular, scheduling problems on a single machine have been widely studied in many facets that address the learning effect and diverse processing environments or time-dependent processing scheduling. Motivated by this observation, we introduce a variant based on the industrial procedure consideration; that is, owing to due date pressure, the processing time of the remaining jobs should be shortened after a period of manufacturing process. We consider a new single-machine scheduling problem with step-improving processing times where the objective function is to find a schedule to minimize a linear combination of the total weighted completion time and total tardiness of all jobs. The proposed problem without a critical date is an NP-hard problem. Therefore, a mixed integer programming model as well as a branch-and-bound (B&B) along with several dominance properties and a lower bound on the completion of an active partial schedule is utilized for solving the problem under study. Subsequently, four variants of the water wave optimization algorithm and four variants of the simulated annealing algorithms were proposed to solve this problem. The simulation results showed that the branch-and-bound method can solve instance problems for up to twelve jobs. The results also showed that all four variants of the wave optimization algorithm did not perform uniformly better than all three variants of the simulated annealing algorithms.

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

IntroductionThe COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients.MethodsIn this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified.ResultsThe ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795).ConclusionsOur findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.

An improved invasive weed optimization enabled Shepard convolutional neural network for classification of breast cancer

AbstractOne of the most predominant types of cancer affecting women is breast cancer. In computer‐assisted analysis of pathology, the most crucial task is classifying breast cancer automatically from the histopathological images. Most of the patients experienced everlasting effects and loss of life because of the absence of timely detection and thereby delay in treatment. Thus, the timely detection as well as precise identification of breast cancer using the histopathological image is highly required. An improved invasive weed water wave optimization (IIWWWO) algorithm is devised here, which is employed in training the Shepard convolutional neural networks (ShCNN) classifier. The developed optimization algorithm combines the improved invasive weed optimization algorithm and water wave optimization algorithm. Initially, the Bayesian filtering process is applied in the input image to remove the noises present in the input image. Moreover, the Hessian‐based Laplacian of Gaussian texture feature model is utilized in segmenting blood cells for an effective classification process. The shape features, namely shape area, diameter, and perimeter, are effectively extracted for better performance. Besides, ShCNN classifier is the most important one for classifying breast cancers. The proposed IIWWWO optimization technique is utilized in tuning the employed classifier termed as ShCNN classifier. Classification of breast cancer is accomplished using two levels, such as malignant and benign. The developed IIWWWO‐based ShCNN model is compared with various metrics, like accuracy, true negative rate (TNR) and true positive rate (TPR). The developed technique used to classify breast cancer obtained better accuracy of 95.83%, maximal TPR of 94.72%, and high TNR of 93.15%.

An ensemble discrete water wave optimization algorithm for the blocking flow-shop scheduling problem with makespan criterion

An ensemble discrete water wave optimization algorithm for the blocking flow-shop scheduling problem with makespan criterion

An effective water wave optimization algorithm with problem-specific knowledge for the distributed assembly blocking flow-shop scheduling problem

The distributed assembly blocking flow-shop scheduling problem (DABFSP), which is a promising area in modern supply chains and manufacturing systems, has attracted great attention from researchers and practitioners. However, minimizing the total tardiness in DABFSP has not captured much attention so far. For solving the DABFSP with the total tardiness criterion, a mixed integer linear programming method is utilized to model the problem, wherein the total tardiness during the production process and assembly process are optimized simultaneously. A constructive heuristic (KBNEH) and a water wave optimization algorithm with problem-specific knowledge (KWWO) are presented. KBNEH is designed by combining a new dispatching rule with an insertion-based improvement procedure to obtain solutions with high quality. In KWWO, effective technologies, such as the re-developed destruction–construction​ operator, four local search methods under the framework of the variable neighborhood search strategy (VNS), the path-relinking method are applied to improve the performance of the algorithm. Comprehensive numerical experiments based on 900 small-scale benchmark instances and 810 large-scale benchmark instances are conducted to evaluate the performance of the presented algorithm. The experimental results obtained by KWWO are 1 to 4 times better than those obtained by the other comparison algorithms, which demonstrate that the effectiveness of KWWO is superior to the compared state-of-the-art algorithms for the considered problem.

H-infinity Controller Based Disturbance Rejection in Continuous Stirred-Tank Reactor

This paper offers an H-infinity (H∞) controller-based disturbance rejection along with the utilization of the water wave optimization (WWO) algorithm. H∞ controller is used to synthesize the guaranteed performance of certain applications as well as it provides maximum gain at any situation. The proposed work focuses on the conflicts of continuous stirred-tank reactor (CSTR) such as variation in temperature and product concentration. The elimination of these issues is performed with the help of the WWO algorithm along with the controller operation. In general, the algorithmic framework of WWO algorithm is simple, and easy to implement with a small-size population and only a few control parameters. The planned work gives the enhanced performance by means of disturbance rejection when compared with the PID, ADRC and ANN controllers. Additionally, the proposed work improves the lifespan of the offered application through the elimination disorders. The overall process is implemented in the MATLAB working platform and the results are compared with the preceding methods to show the expected performance.

CNN Features and Optimized Generative Adversarial Network for COVID-19 Detection from Chest X-Ray Images.

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.