Intelligent Water Drops Algorithm Research Articles

An intelligent water drop algorithm with deep learning driven vehicle detection and classification

<abstract> <p>Vehicle detection in Remote Sensing Images (RSI) is a specific application of object recognition like satellite or aerial imagery. This application is highly beneficial in different fields like defense, traffic monitoring, and urban planning. However, complex particulars about the vehicles and the surrounding background, delivered by the RSIs, need sophisticated investigation techniques depending on large data models. This is crucial though the amount of reliable and labelled training datasets is still a constraint. The challenges involved in vehicle detection from the RSIs include variations in vehicle orientations, appearances, and sizes due to dissimilar imaging conditions, weather, and terrain. Both specific architecture and hyperparameters of the Deep Learning (DL) algorithm must be tailored to the features of RS data and the nature of vehicle detection tasks. Therefore, the current study proposes the Intelligent Water Drop Algorithm with Deep Learning-Driven Vehicle Detection and Classification (IWDADL-VDC) methodology to be applied upon the Remote Sensing Images. The IWDADL-VDC technique exploits a hyperparameter-tuned DL model for both recognition and classification of the vehicles. In order to accomplish this, the IWDADL-VDC technique follows two major stages, namely vehicle detection and classification. For vehicle detection process, the IWDADL-VDC method uses the improved YOLO-v7 model. After the vehicles are detected, the next stage of classification is performed with the help of Deep Long Short-Term Memory (DLSTM) approach. In order to enhance the classification outcomes of the DLSTM model, the IWDA-based hyperparameter tuning process has been employed in this study. The experimental validation of the model was conducted using a benchmark dataset and the results attained by the IWDADL-VDC technique were promising over other recent approaches.</p> </abstract>

Improved junction-based routing for VANETs using a Bio-inspired route stability approach

The Junction-Based Routing (JBR) is established as the adequate solution to data packet routing within an urban Vehicular Ad Hoc Network (VANET). The latter is characterized by radio obstacles, unreadable traffic density change, and unstable vehicular mobility speed. Existing JBR routing protocols have not defined specific evaluation mechanisms to anticipate and detect stable road segments in large urban areas yet. Consequently, the connectivity of selected streets for data forwarding may not be regularly reliable for long periods, especially for remote destinations. As a matter of fact, stable long-lifespan JBR paths cannot be discovered; hence, generating low Packet Delivery Ratio (PDR) performances. In this paper, we introduce swarm-inspired optimization as a novel solution to reduce the number of lost data packets on the basis of a new route stability notion. A bio-inspired metaheuristic technique named the Intelligent Water Drops (IWD) algorithm is applied to track reactively and proactively the routing consistency of road segments. IWD seeks to gather accurately unreachable road segments’ up-to-date connectivity information in order to predict progressively their stability degree. The latter is deduced according to predefined routing parameters featuring the history of route traffic and data forwarding, and vehicles’ speed. These parameters are utilized to trace direct JBR paths to data packets’ destinations with minimum disconnected road segments following IWD-inspired Dijkstra JBR paths. The proposed bio-inspired JBR protocol is compared to a few notable JBR schemes in a city map scenario. The simulation results showed a consistent mean PDR progress between 2.55% and 33.47% of all tested simulation variants.

Hybrid ant colony and intelligent water drop algorithm for route planning of unmanned aerial vehicles

Route planning is a crucial element in unmanned aerial vehicle (UAV) systems, particularly in autonomous UAV technology. In the past decades, various algorithms have been proposed for UAV route planning. However, they still have defects, such as stagnation and slow search rates. In this study, a novel hybrid algorithm which integrates ant colony optimization (ACO) and intelligent water drop (IWD) is proposed for UAV route planning. First, the advantages of the IWD and ACO algorithms are combined in an iterative strategy, to ensure mutual cooperation via exchange of information for route optimization. Initially, the water drops optimize the soil and pheromones within the environment simultaneously to generate good approximate solutions and an initial pheromone distribution for the ant colony. Based thereupon, the ant colony roams the solution space to further optimize the routes, thus combining the exploration potential and exploitability of two types of agents. Additionally, a novel node selection strategy is proposed to guide the agents’ route planning along a reasonable direction. Compared with state-of-the-art algorithms, the convergence accuracy, success rate, and stability of the proposed algorithm exhibited significant improvements of approximately 8.25%, 4.20%, and 66.20%, respectively.

On Enhanced Intelligent Water Drops Algorithm for Travelling Salesman Problem under Uncertain Paradigm

Abstract Travelling salesman problem (TSP) is a well known combinatorial optimization problem which has drawn colossal attention due to its eclectic range of applications. In this article, we have proposed two modified versions of intelligent water drops (IWD) algorithm. The first one is the enhanced IWD (e-IWD) algorithm to solve single objective TSP. In the second modification, e-IWD algorithm has been extended to enhanced multi-objective IWD(e-MIWD) algorithm for solving multi-objective TSP. In order to achieve a better exploration capability in both of the proposed algorithms, the soil and velocity parameters of a randomly selected water drop are updated after every iteration of the algorithm when it traverses all the intermediate vertices for a tour. The proposed algorithms have been compared with some other existing similar algorithms on different benchmark instances of TSPs. Furthermore, we have addressed the TSP for both single and multiple objectives under uncertain environment.

Improved intelligent water drop-based hybrid feature selection method for microarray data processing

Classifying microarray datasets, which usually contains many noise genes that degrade the performance of classifiers and decrease classification accuracy rate, is a competitive research topic. Feature selection (FS) is one of the most practical ways for finding the most optimal subset of genes that increases classification’s accuracy for diagnostic and prognostic prediction of tumor cancer from the microarray datasets. This means that we always need to develop more efficient FS methods, that select only optimal or close-to-optimal subset of features to improve classification performance. In this paper, we propose a hybrid FS method for microarray data processing, that combines an ensemble filter with an Improved Intelligent Water Drop (IIWD) algorithm as a wrapper by adding one of three local search (LS) algorithms: Tabu search (TS), Novel LS algorithm (NLSA), or Hill Climbing (HC) in each iteration from IWD, and using a correlation coefficient filter as a heuristic undesirability (HUD) for next node selection in the original IWD algorithm. The effects of adding three different LS algorithms to the proposed IIWD algorithm have been evaluated through comparing the performance of the proposed ensemble filter-IIWD-based wrapper without adding any LS algorithms named (PHFS-IWD) FS method versus its performance when adding a specific LS algorithm from (TS, NLSA or HC) in FS methods named, (PHFS-IWDTS, PHFS-IWDNLSA, and PHFS-IWDHC), respectively. Naïve Bayes(NB) classifier with five microarray datasets have been deployed for evaluating and comparing the proposed hybrid FS methods. Results show that using LS algorithms in each iteration from the IWD algorithm improves F-score value with an average equal to 5% compared with PHFS-IWD. Also, PHFS-IWDNLSA improves the F-score value with an average of 4.15% over PHFS-IWDTS, and 5.67% over PHFS-IWDHC while PHFS-IWDTS outperformed PHFS-IWDHC with an average of increment equal to 1.6%. On the other hand, the proposed hybrid-based FS methods improve accuracy with an average equal to 8.92% in three out of five datasets and decrease the number of genes with a percentage of 58.5% in all five datasets compared with six of the most recent state-of-the-art FS methods.

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

Path Planning of Inspection Robot Based on Improved Intelligent Water Drop Algorithm

Path Planning of Inspection Robot Based on Improved Intelligent Water Drop Algorithm

A novel meta-heuristic algorithm based optimized load frequency controller

This work proposes a 2-degree-of-freedom proportional-integral-double-derivative (2-DOF PIDD) controller to improve frequency profiles of an IEEE 39 bus 10 generator 3 area New England interconnected power system during step load perturbations. To increase the system’s dynamic response, parallel derivative components are used in the secondary controller mechanism, and comparisons are made with proportional, integral and derivative (PID) and 2-DOF PID controllers to illustrate the advantage of the proposed controller scheme. The ideal gain values of the PID, 2-DOF PID, and 2-DOF PIDD controllers are achieved using a novel evolutionary algorithm called the intelligent water drops (IWD) algorithm, which is used for the first time for the load frequency control problem for a 3-area power system. The results proved that the proposed controller is superior to the existing controllers. Simulations are done in the MATLAB-Simulink® environment.

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Abstract A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.

Modelling and control of fuzzy‐based systems using intelligent water drop algorithm

AbstractIdentification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques.

Multi-robot co-operation for stick carrying application using hybridization of meta-heuristic algorithm

The paper offers a novel technique for resolving the multi-robot cooperation for stick carrying applications. The problem addresses the computation of a collision-free optimal path from a predefined initial position to target position during transportation of stick or object cooperatively by robot pair in the multi-robot environment. The stick carrying application has been resolved by embedding the modified Q-learning into the hybrid process of an improved version of particle swarm optimization and intelligent water drop algorithm. In the present context, modified Q-learning generates the best solution for particle swarm optimization and particle swarm optimization is upgraded through the perception of cubic spline and generate the optimal position in the successive iteration using intelligent water drop algorithm and also enhances the intensification and diversification capability of particle swarm optimization. The proposed hybrid algorithm computes the collision-free subsequent position for each robot pair by avoiding the obstacles in its path, avoiding the trapping at the local optima, improving the convergence speed, optimizing the path distance for every pair of robots, energy usage and path smoothness both in the static and dynamic environment’s. The validation of the proposed hybrid algorithm has been verified and checked the robustness of the algorithm through computer simulation and real robots through Webots simulator. Further, the efficiency of the proposed algorithm has been verified by comparing the result obtained proposed algorithm and its competitor algorithms and comparing the result of the proposed algorithm with the existing state-of arts. The comparison result shows that the proposed algorithm is superior to its competitor algorithms and state of arts for different matrices.

Solving software project scheduling problem using grey wolf optimization

In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project’s duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO.

An Enhanced Forwarding Method based on Intelligent Water Drops Algorithm in Named Data Network

Although Named Data Network (NDN) has made a bright future in Internet for high volume of requests by many users, how to send a request package (I-Pkt) consciously from the consumer to the Producer and returning the data package (D-Pkt) inversely is still one of its most important challenges. According to the recent limited researches, using service quality parameters beside an optimization algorithm like ant colony to find the optimal path has been an appropriate response to solve this problem. Not considering the service quality parameters sufficiently to find an optimal path is a problem to be focused more on. In line with this, this study has addressed the Intelligent Water Drops (IWD) optimization algorithm. In this algorithm, the best possible path based on many parameters related to path quality like cost, delay and number of steps taken by the package (I-Pkt) will be selected. In other words, the water drop movement tries to find the optimal path through the amount of soil collection in the path or the velocity rate. The results of simulating in ndnSIM simulator show that comparing the ant colony, it has improved in cost parameter on average by 23%, in hit ratio parameter by 24% and in hop count parameter by 13%. The general result is increasing service level quality in NDNs.

A Novel Hybrid IWD Algorithm with Extreme Learning Adaptive Neuro-fuzzy Inference System based Follicle Identification for Polycystic Ovary Syndrome in Ultrasound Images

The ovary's production of many follicles produces PCOS (Polycystic Ovary Syndrome) is a condition that affects women. This article developed an automated diagnostic approach for detecting follicles in the ovary utilizing ultrasound (US) images during infertility treatment. This approach includes procedures including pre-processing, segmentation, feature extraction, and classification. Weiner filter is utilized for eliminating salt and pepper noise, expectation and maximization technique is applied for segmenting follicles, texture-based and statistical features are extracted, and at last, the ovary is classified as normal and abnormal utilizing Hybrid Intelligent Water Drop (IWD) with Extreme learning adaptive neuro-fuzzy inference system (HIWD-ELANFIS) approach. Then, the severity of the disorder is identified as severe, moderate or normal by applying this new method on US images of ovaries. To determine the efficiency of this strategy, the findings are contrasted with those of other conventional methods.

Multi-spectral images classification based on intelligent water drops algorithm

Mosul's city land covers soil, cultivated land, stony, pastoral land, water, and ploughed agricultural land. We have classified multispectral images captured by the sensor (TM) carried on the Landsat satellite. Integrated approach of intelligent water drops (IWDs) algorithm is used to identify natural terrain. In this research, IWDs have been suggested to find the best results for multispectral image classification. The purpose of using an algorithm, give accurate and fast results by comparing the IWD algorithm with the K-mean algorithm. The IWD algorithm is programmed using the Matlab2017b software environment to demonstrate the proposed methodology's effectiveness. The proposed integrated concept has been applied to satellite images of Mosul city in Iraq. By comparing the IWD with the K-mean, we found clear time superiority of the IWD algorithm, equal 1.4122 with (K-mean) time equal 18.9475. Furthermore, the water drop algorithm's classification accuracy is 95%, while the K-mean classification accuracy is 83.3%. Based on the analysis and results, we conclude the IWD is a robust promising and approach to detecting remote sensing image changes and multispectral image classification.

Intelligent Water Drop Algorithm based Web Page Recommendation Using Linear Regression Learning

Analysis of website visitor for improving the performance of user experience on web portal is primary requirement in this online world. Hence number of web-based feature was used for the study of user behavior and recommend page accordingly. This research work presents the utilization of weblog feature for analysis and pattern learning. Proposed model extract association rule forms the weblog with text pattern format on the web page. Here we have considered the input data set from a live website’s weblogs called as project tunnel.com. Association rule and text URL patterns were used for learning multi-Regression probability generation. Once the regression value is set than Intelligent Water Drop Algorithm will be applied for predicting or recommending the next page as per user previous set of pages, where IWD algorithm increases the accuracy of prediction by utilizing the regression values in fitness function. Live websites weblogs were analyzed for the experiment and comparison of proposed WPPIWD (Web Page Prediction Intelligent Water Drop) has improved the work efficiency when compared to other existing methods.

Technical-economic framework for designing of water pumping system based on photovoltaic clean energy with water storage for drinking application.

In this paper, the technical-economic framework for designing of water pumping system based on photovoltaic clean energy with water tank storage is presented to supply drinking water of customers for remote areas. The objective function is to minimize the net present cost (NPC) (as economic index) including initial investment costs, maintenance, and replacement costs, and reliability constraint is defined as customer's water not supplied probability (CWNSP) as technical index. A meta-heuristic intelligent water drops algorithm (IWDA) is proposed to optimize the solar water pumping system considering NPC and CWNSP with high accuracy and speed of optimization in achieving the global solution. The simulation results show that the proposed method is capable of responding to customer's water demand by optimally sizing components and water storage tank based on IWDA which is inspired based on flowing the water drops in rivers by achieving the lowest cost with optimal reliability. The NPC of the system with CWNSP equal to 3.17 % is obtained 0.24 M$ for 6-m-high water extraction. The results showed that with increasing the water extraction height, the NPC increased, and the reliability also weakened. Moreover, the superiority of the IWDA is confirmed compared with particle swarm optimization (PSO) in designing a water pumping system with the lowest NPC.

Hybrid intelligent water Drops algorithm for examination timetabling problem

The current study investigates the Intelligent Water Drops (IWD) metaheuristic algorithm to construct and produce good quality solutions for the university examination timetabling problem (UETP). The IWD is a population-based metaheuristic that simulates the dynamic of the river systems. The main motivations for investigating IWD algorithm for examination timetabling problem is the ability to explore the search space effectively. The main drawback of IWD algorithm is like other population-based algorithm in exploitation process where it is very efficient scanning several search space niches, but it is unable to drilling down in each niche to which it navigates. In this paper we propose a hybrid approach based on IWD and locale search algorithm to improve the exploitation of IWD algorithm. The experimental results demonstrated that the proposed algorithm (i.e., Hybrid IWD) obtained best results in three datasets when comparing with the best-known results performed by the swarm intelligent approaches. Finally, the proposed algorithm achieved one best results in comparison with the other metaheuristic approaches.

Adaptive IWD-based algorithm for deployment of business processes into cloud federations

PurposeThe concept of business process (BP) as a service is a new solution in enterprises for the purpose of using specific BPs. BPs represent combinations of software services that must be properly executed by the resources provided by a company’s information technology infrastructure. As the policy requirements are different in each enterprise, processes are constantly evolving and demanding new resources in terms of computation and storage. To support more agility and flexibility, it is common today for enterprises to outsource their processes to clouds and, more recently, to cloud federation environment. Ensuring the optimal allocation of cloud resources to process service during the execution of workflows in accordance with user policy requirements is a major concern. Given the diversity of resources available in a cloud federation environment and the ongoing process changes required based on policies, reallocating cloud resources for service processing may lead to high computational costs and increased overheads in communication costs.Design/methodology/approachThis paper presents a new adaptive resource allocation approach that uses a novel algorithm extending the natural-based intelligent water drops (IWD) algorithm that optimizes the resource allocation of workflows on the cloud federation which can estimate and optimize final deployment costs. The proposed algorithm is implemented and embedded within the WokflowSim simulation toolkit and tested in different simulated cloud environments with different workflow models.FindingsThe algorithm showed noticeable enhancements over the classical workflow deployment algorithms taking into account the challenges of data transfer. This paper made a comparison between the proposed IWD-based workflow deployment (IWFD) algorithm with other proposed algorithms. IWFD presented considerable improvements in the makespan, cost and data transfer in most situations in the cloud federation environment.Originality/valueAn extension for WorkflowSim to support the implementation of BPs in a federation cloud space regarding BP policy. Optimize workflow execution performance in Federated clouds by means of IWFD algorithm.

System Design of MEB in M-IWD Model with Heuristic Function on WSN

Background: The Modified Intelligent Water Drop algorithm incorporated with the proposed heuristic function to enhance the characteristics of randomness, individual diversity to minimize the total energy required to broadcast the data from each sensor node towards the sink node in a network. Objective: The Modified Intelligent Water Drop Algorithm has been designed to achieve the divergence to find out an optimal Minimum Energy Broadcasting tree in WSN. Methods: The proposed variant has been evaluated and compared concerning contemporary Evolutionary techniques using appropriate performance criteria. Results: To achieve optimum result, the proposed Modified Intelligent Water Drop algorithm compared with existing algorithm along with 20 nodes dataset with 30 instances, 50 nodes dataset with 30 instances and 100 nodes dataset with 30 instances. Conclusion: In this perspective, a suitable experimental setup has been designed and experiments are performed on different classes of Minimum Energy Broadcasting instances obtained from standard Minimum Energy Broadcasting library [Comopt 2012] to validate the proposed Modified Intelligent Water Drop Algorithm. The simulation results of MEB for MIWD-HUD with convergence and divergence is given.