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Journal articles on the topic 'Intelligent Water Drop Algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 7 July 2025

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1

AlDeeb, Bashar Abedal Mohdi, Norita Md Norwawi, and Mohammed A. Al-Betar. "A Survey on Intelligent Water Drop Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (2014): 5075–84. http://dx.doi.org/10.24297/ijct.v13i10.2329.

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In the optimization areas, there are different algorithms that have been applied such as swarm intelligence algorithms. The researchers have found different algorithms by simulating the behaviors of various swarms of insects and animals such as fishes, bees, and ants. The intelligent water drops algorithm is one of the recently developed algorithms in the swarm intelligence field; this algorithm mimicked the dynamic of river systems. The natural water drops used to develop Intelligent Water Drop (IWD) algorithm. Therefore, the mechanisms that happen in rivers have inspired the researchers mainly to create new algorithms. IWD is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to a better drops (or solutions). This paper presents recent developments of the IWD algorithms in terms of theory and application. This paper concludes many of research directions that are necessary for the future of IWD algorithm.

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2

Wang, Tao, Jing Ni, and Yixuan Wang. "An Intelligent Water Drop Algorithm for Solving Multi-Objective Vehicle Routing Problems With Mixed Time Windows." International Journal of Strategic Decision Sciences 10, no. 1 (2019): 82–104. http://dx.doi.org/10.4018/ijsds.2019010106.

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This article proposes an Intelligent Water Drop Algorithm for solving Multi-Objective Vehicle Routing Problems by considering the constraints of vehicle volume, delivery mileage, and mixed time windows and minimizing the cost of distribution and the minimum number of vehicles. This article improves the basic Intelligent Water Drop Algorithm and show the improved intelligent water droplet genetic hybrid algorithm is an effective method for solving discrete problems. The authors present a practical example and show the applicability of the proposed algorithm. The authors compare the algorithms with the basic algorithm and show the improved intelligent droplet genetic hybrid algorithm has higher computing efficiency and continuous optimization capability.

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Dr.K.Lenin. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED INTELLIGENT WATER DROP ALGORITHM." International Journal of Research - Granthaalayah 5, no. 11 (2017): 116–25. https://doi.org/10.5281/zenodo.1069332.

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In this paper, Improved Intelligent Water Drop (IIW) algorithm has been proposed to solve the optimal reactive power problem. In this work firefly and water drop algorithm has been combined to improve the exploration & exploitation. Fire fly algorithm imitates the firefly light flashing behaviour is an astonishing signal in the sky, usually found in tropical and temperate regions.  Water drop algorithm contains a few necessary elements of natural water drops and action and reaction that occur between river bed & the water drops that flow within.  Proposed Improved Intelligent Water Drop (IIW) algorithm has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

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Chen, Gang, Xiaoyuan Wu, Jinghua Li, and Hui Guo. "Green Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center Based on Improved Intelligent Water Drop Algorithms." Mathematical Problems in Engineering 2020 (January 9, 2020): 1–13. http://dx.doi.org/10.1155/2020/9839634.

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The timeliness of the steel distribution center process contributes to the smooth progress of ship construction. However, carbon emissions from vehicles in the distribution process are a major source of pollution. Reasonable vehicle routing and scheduling can effectively reduce the carbon emissions of vehicles and ensure the timeliness of distribution. To solve this problem, a green vehicle routing and scheduling problem model with soft time windows was proposed in this study. An intelligent water drop algorithm was designed and improved and then compared with the genetic algorithm and the traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm was demonstrated. Finally, this algorithm was applied to a specific example to demonstrate that the improved intelligent water drop algorithm effectively reduced the cost of such green vehicle problems, thus reducing the carbon emissions of vehicles during the distribution process and achieving reductions in environmental pollution. Ultimately, this algorithm facilitates the achievement of green shipbuilding.

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Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED INTELLIGENT WATER DROP ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 11 (2017): 116–25. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2335.

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In this paper, Improved Intelligent Water Drop (IIW) algorithm has been proposed to solve the optimal reactive power problem. In this work firefly and water drop algorithm has been combined to improve the exploration & exploitation. Fire fly algorithm imitates the firefly light flashing behaviour is an astonishing signal in the sky, usually found in tropical and temperate regions. Water drop algorithm contains a few necessary elements of natural water drops and action and reaction that occur between river bed & the water drops that flow within. Proposed Improved Intelligent Water Drop (IIW) algorithm has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

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6

Li, Jinghua, Hui Guo, Qinghua Zhou, and Boxin Yang. "Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center under Green Shipbuilding Mode." Sustainability 11, no. 15 (2019): 4248. http://dx.doi.org/10.3390/su11154248.

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Timeliness of steel distribution centers can effectively ensure the smooth progress of ship construction, but the carbon emissions of vehicles in the distribution process are also a major source of pollution. Therefore, when considering the common cost of vehicle distribution, taking the carbon emissions of vehicles into account, this paper establishes a Mixed Integer Linear Programming (MILP) model called green vehicle routing and scheduling problem with simultaneous pickups and deliveries and time windows (GVRSP-SPDTW). An intelligent water drop algorithm is designed and improved, and compared with the genetic algorithm and traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm is proven. Finally, it is applied to a specific example to prove that the improved intelligent water drop algorithm can effectively reduce the cost of such problems, thereby reducing the carbon emissions of vehicles in the distribution process, achieving the goals of reducing environmental pollution and green shipbuilding.

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7

Abdous, Majid, and Touraj Banirostam. "Cloud Computing Intelligent Management by Metaheuristic Algorithm of Intelligent Water Drop." IJARCCE 5, no. 3 (2016): 1–6. http://dx.doi.org/10.17148/ijarcce.2016.5301.

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8

Shah‐Hosseini, Hamed. "Intelligent water drops algorithm." International Journal of Intelligent Computing and Cybernetics 1, no. 2 (2008): 193–212. http://dx.doi.org/10.1108/17563780810874717.

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9

Siddique, Nazmul, and Hojjat Adeli. "Water Drop Algorithms." International Journal on Artificial Intelligence Tools 23, no. 06 (2014): 1430002. http://dx.doi.org/10.1142/s0218213014300026.

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This article presents a review of a novel nature-inspired adaptive optimization algorithm known as Water Drop Algorithm (WDA) which imitates the dynamics of river systems and the behavior of water drops when they are moving in a river system such as the variation of velocity, the change of sediment/soil in the river bed, the change of direction of the flow, and so on. Recent improved, modified, and adaptive WDAs are examined in this paper. Various applications of WDA are described briefly including the vehicle routing problem, economic load dispatch problem, economic and emission dispatch problem, optimal data aggregation tree in wireless networks, reactive power dispatch problem service selection, and reservoir operation.

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10

Luangpaiboon, Pongchanun. "Optimisation of Manufacturing Process Models via Intelligent Water Drop Algorithm." Applied Mechanics and Materials 217-219 (November 2012): 1501–5. http://dx.doi.org/10.4028/www.scientific.net/amm.217-219.1501.

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In this paper, an intelligent water drop algorithm or IWD has been developed to optimise machining parameters in turning operation including a spring force model. Firstly, machining conditions are to minimise the production cost in conventional manufacturing processes. Several passes of rough machining are started on the turning operation with a final pass of a finishing. Various constraints are considered in each non-linear and non-convex model. The machining parameters in the turning consist of the depth of cut, cutting speed and feed. Finally, in a specialised manufacturing application on the spring force problem, an achievement of a specific goal may be the primary objective subject to some process parameter ranges. The computational results clearly showed that the proposed sequential procedures of the IWD have considerably improved the objective functions.

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Gao, Bo, Xiaoyu Hu, Zhenrui Peng, and Yubo Song. "Application of intelligent water drop algorithm in process planning optimization." International Journal of Advanced Manufacturing Technology 106, no. 11-12 (2020): 5199–211. http://dx.doi.org/10.1007/s00170-019-04850-4.

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12

Shahbaa, I. Khaleel, and W. Khaled Ragad. "Image retrieval based on swarm intelligence." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5390–401. https://doi.org/10.11591/ijece.v11i6.pp5390-5401.

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To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

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13

Halder, Swapna, Haresh Kumar Sharma, Arindam Biswas, Olegas Prentkovskis, Saibal Majumder, and Paulius Skačkauskas. "On Enhanced Intelligent Water Drops Algorithm for Travelling Salesman Problem under Uncertain Paradigm." Transport and Telecommunication Journal 24, no. 3 (2023): 228–55. http://dx.doi.org/10.2478/ttj-2023-0019.

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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.

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14

Khaleel, Shahbaa I., and Ragad W. Khaled. "Image retrieval based on swarm intelligence." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5390. http://dx.doi.org/10.11591/ijece.v11i6.pp5390-5401.

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To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.

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15

Thilagavathi, Ms D., and Dr Antony Selvadoss Thanamani. "Scheduling in High Performance Computing Environment using Firefly Algorithm and Intelligent Water Drop Algorithms." International Journal of Engineering Trends and Technology 14, no. 1 (2014): 8–12. http://dx.doi.org/10.14445/22315381/ijett-v14p203.

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16

Marrwa, Abd-AlKareem Alabajee, Rafaa Ahmed Dena, and Riyadh Alreffaee Taghreed. "Solving software project scheduling problem using grey wolf optimization." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 6 (2021): 1820–29. https://doi.org/10.12928/telkomnika.v19i6.16758.

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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.

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S, Nithyanandh, and Jaiganesh V. "Quality of service enabled intelligent water drop algorithm based routing protocol for dynamic link failure detection in wireless sensor network." Indian Journal of Science and Technology 13, no. 16 (2020): 1641–47. https://doi.org/10.17485/IJST/v13i16.19.

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Abstract <strong>Objectives:</strong> To propose a better routing protocol method to increase the lifespan of the network and to make the sensed data to reach from source to destination without any data loss. <strong>Methods:</strong> This study proposes a routing protocol inspired by nature namely, Quality of Service enabled Intelligent Water Drop Algorithm based Routing Protocol to detect link failures and provide efficient routing.<strong> Findings:</strong> Comparison has been made with baseline protocols to check its effectiveness and found that the proposed protocol has better performance than the baseline protocols. This research work enhances and adopts the intelligent water drop algorithm with the properties of velocity of water drops and water drops carrying level of soil for finding the better stable route for communication and detecting the link failures dynamically. The results determine that the proposed routing protocol is able to perform better than existing protocols and methods in terms of Energy Depletion rate, Survivability of nodes during transmission, delivering the packets from one end to another end, delay, failure tolerance and increase in Network Lifespan. <strong>Keywords:</strong> WSN; Delay; Energy; Routing; Water-Drop

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18

Alhenawi, Esra’a, Hadeel Alazzam, Rizik Al-Sayyed, Orieb AbuAlghanam, and Omar Adwan. "Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm." Cybernetics and Information Technologies 22, no. 4 (2022): 73–90. http://dx.doi.org/10.2478/cait-2022-0040.

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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.

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Cheng, Hanlie, and Qiang Qin. "Design of Intelligent Control Systems for Layered Water Injections in Oilfields." International Journal of Distributed Systems and Technologies 15, no. 1 (2024): 1–13. http://dx.doi.org/10.4018/ijdst.342097.

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With rapid socio-economic growth and increased energy demand, exploration and exploitation of oil and gas resources have become crucial. Long-term exploitation leads to problems such as pressure drop and production reduction in oil fields, and water injection technology has become a common method to improve these problems. The traditional direct water injection for oil extraction has problems such as high injection cost and low oil recovery efficiency. Therefore, an intelligent control system for different oilfield reservoirs is needed. This study focuses on the layered water injection intelligent system based on advanced sensor technology, digital signal processing and intelligent algorithms. The article reviews the advantages of layered water injection system and the current research status, designs an intelligent control structure including hardware circuits and modular software processes, and adopts adaptive particle swarm optimization algorithm as the core of intelligent control.

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Khaleel, Almas Ahmed. "Multi-spectral images classification based on intelligent water drops algorithm." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 116. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp116-125.

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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.

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Khaleel, Almas Ahmed. "Multi-spectral images classification based on intelligent water drops algorithm." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 116–25. https://doi.org/10.11591/ijeecs.v24.i1.pp116-125.

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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 MATLAB 2017b 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 (kmean) 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.

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22

Lenin, K., and M. Surya Kalavathi. "An Intelligent Water Drop Algorithm for Solving Optimal Reactive Power Dispatch Problem." International Journal on Electrical Engineering and Informatics 4, no. 3 (2012): 450–63. http://dx.doi.org/10.15676/ijeei.2012.4.3.6.

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23

Li, Chunquan, Ming Zhang, Yuling Shang, and Yuwei Hu. "Research on the cable routing optimisation by hybrid intelligent water drop algorithm." International Journal of Materials and Structural Integrity 12, no. 1/2/3 (2018): 208. http://dx.doi.org/10.1504/ijmsi.2018.093910.

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Hu, Yuwei, Yuling Shang, Chunquan Li, and Ming Zhang. "Research on the cable routing optimisation by hybrid intelligent water drop algorithm." International Journal of Materials and Structural Integrity 12, no. 1/2/3 (2018): 208. http://dx.doi.org/10.1504/ijmsi.2018.10014947.

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25

Alijla, Basem O., Chee Peng Lim, Li-Pei Wong, Ahamad Tajudin Khader, and Mohammed Azmi Al-Betar. "An ensemble of intelligent water drop algorithm for feature selection optimization problem." Applied Soft Computing 65 (April 2018): 531–41. http://dx.doi.org/10.1016/j.asoc.2018.02.003.

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Luis, Antonio Moncayo-Martínez. "Supply chain design using a modified IWD algorithm." Revista Facultad de Ingeniería –redin-, no. 84 (September 25, 2017): 9–16. https://doi.org/10.17533/udea.redin.n84a02.

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The Intelligent Water Drop (IWD) algorithm is inspired by the movement of real water drops in a river. A water drop could find an optimum path to a lake or sea by interacting with the conditions of its surroundings. In the process of reaching such destination, the water drops interact with the river bed while they move through it. Similarly, the supply chain problem can be modelled as a flow of supply, manufacturing, and delivery stages that must be completed to produce a finished product and then to deliver it to the end user. The problem is to select one option that carries out the stage, e.g. for a supply stage, many suppliers could supply the component represented by it. As each stage is characterised by its time and cost, multi--objective optimisation algorithm is used to minimise the time to market and production cost, simultaneously. Focusing on this analogy, this paper proposes an approach to the supply chain problem using a multi--objective extension to the intelligent water drops algorithm. Artificial water drops, flowing through the supply chain, will simultaneously minimise the production cost and the time to market of every product in a generic BOM by using the concept of Pareto optimality. A widely-used notebook supply chain in literature is solved. We provide some performance metrics of the solution and compare the Pareto set computed by the proposed algorithm with the one returned by exhaustive enumeration.

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Kaur, Surinder, Gopal Chaudhary, Javalkar Dinesh Kumar, et al. "Optimizing Fast Fourier Transform (FFT) Image Compression using Intelligent Water Drop (IWD) Algorithm." International Journal of Interactive Multimedia and Artificial Intelligence IP, IP (2022): 1. http://dx.doi.org/10.9781/ijimai.2022.01.004.

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Rama Prabha, D., T. Jayabarathi, R. Umamageswari, and S. Saranya. "Optimal location and sizing of distributed generation unit using intelligent water drop algorithm." Sustainable Energy Technologies and Assessments 11 (September 2015): 106–13. http://dx.doi.org/10.1016/j.seta.2015.07.003.

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Vaiyapuri, Thavavel, M. Sivakumar, Shridevi S, et al. "An intelligent water drop algorithm with deep learning driven vehicle detection and classification." AIMS Mathematics 9, no. 5 (2024): 11352–71. http://dx.doi.org/10.3934/math.2024557.

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<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>

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ElMustafa, Saba, Ameera Jaradat, Iyad Abu Doush, and Nahed Mansour. "Community detection using intelligent water drops optimisation algorithm." International Journal of Reasoning-based Intelligent Systems 9, no. 1 (2017): 52. http://dx.doi.org/10.1504/ijris.2017.086140.

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Doush, Iyad Abu, Nahed Mansour, Saba ElMustafa, and Ameera Jaradat. "Community detection using intelligent water drops optimisation algorithm." International Journal of Reasoning-based Intelligent Systems 9, no. 1 (2017): 52. http://dx.doi.org/10.1504/ijris.2017.10007137.

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Msallam, Mohammed M., and Mohammad Hamdan. "Improved intelligent water drops algorithm using adaptive schema." International Journal of Bio-Inspired Computation 3, no. 2 (2011): 103. http://dx.doi.org/10.1504/ijbic.2011.039909.

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Bahrami, Amir, and Saeed Reza Ostadzadeh. "Back scattering response from single, finite and infinite array of nonlinear antennas based on intelligent water drops algorithm." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 6 (2019): 2040–56. http://dx.doi.org/10.1108/compel-08-2018-0317.

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Purpose The purpose of this paper is to calculate the back scattering response from single, finite and infinite arrays of nonlinear antennas like the case where the antennas are exposed to high-value signals such as lightning strokes. Design/methodology/approach In this paper, the authors have used a recently introduced optimization technique called intelligent water drop. Findings The results exhibit that the method used by the authors is faster and more accurate than other conventional optimization algorithms, i.e. particle swarm optimization and genetic algorithm. Originality/value A new optimization algorithm is used to solve nonlinear problem accurately and sufficiently. Although the technique is not confined to the mentioned examples in the paper, it can be applied to other nonlinear circuits.

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Dehariya, Ashish Kumar, and Pragya Shukla. "Brain Image Segmentation to Diagnose Tumor by Applying Wiener Filter and Intelligent Water Drop Algorithm." International Journal of Computer Theory and Engineering 13, no. 3 (2021): 84–90. http://dx.doi.org/10.7763/ijcte.2021.v13.1294.

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Sun, Xixia, Su Pan, Nan Bao, and Ning Liu. "Hybrid ant colony and intelligent water drop algorithm for route planning of unmanned aerial vehicles." Computers and Electrical Engineering 111 (November 2023): 108957. http://dx.doi.org/10.1016/j.compeleceng.2023.108957.

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Dariane, A. B., and S. Sarani. "Application of Intelligent Water Drops Algorithm in Reservoir Operation." Water Resources Management 27, no. 14 (2013): 4827–43. http://dx.doi.org/10.1007/s11269-013-0441-x.

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Abhishek, Kumar Sahu, Sharma Durga, and Mishra Anup. "PV-grid performance improvement through integrated intelligent water drop optimization with neural network for maximum power point tracking." i-manager’s Journal on Electrical Engineering 18, no. 1 (2024): 1. https://doi.org/10.26634/jee.18.1.21181.

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This paper presents an optimized model that combines the Intelligent Water Drop (IWD) optimization algorithm and a neural network (NN) for maximum power point tracking (MPPT) in photovoltaic (PV) applications. The proposed approach demonstrates superior performance compared to conventional methods, including Fuzzy Logic Control, Perturb and Observe (P&O), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Incremental Conductance (INC) control. The enhanced model improves adaptability and convergence due to the optimization capabilities of the IWD algorithm and leverages the predictive characteristics of the NN for faster and more accurate tracking. The results indicate that this model offers significant potential for future-generation PV systems, particularly in solar energy applications.

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Hosseini, Hamed Shah. "Improving K-means clustering algorithm with the intelligent water drops (IWD) algorithm." International Journal of Data Mining, Modelling and Management 5, no. 4 (2013): 301. http://dx.doi.org/10.1504/ijdmmm.2013.057681.

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Hosseini, Hamed Shah. "The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm." International Journal of Bio-Inspired Computation 1, no. 1/2 (2009): 71. http://dx.doi.org/10.1504/ijbic.2009.022775.

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Xuan, Wu, and Zeng Fanquan. "Design of electro-hydraulic servo loading controlling system based on fuzzy intelligent water drop fusion algorithm." Computers & Electrical Engineering 71 (October 2018): 485–91. http://dx.doi.org/10.1016/j.compeleceng.2018.08.010.

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P, Radha, and Sudha Bhuvaneswari N. "Equanimous Intelligent Water Drop Algorithm-Based Feed-Forward Neural Network (EIWDA-FFNN) for Improving Sentiment Analysis." International Journal of Engineering Trends and Technology 71, no. 9 (2023): 341–55. http://dx.doi.org/10.14445/22315381/ijett-v71i9p230.

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Norwick, Ethan L., Adrian T. Caldwell, Vincent M. Roemer, and Felix J. Hargrove. "Intelligent Algorithm-Driven Optimization of Water-Cooled Plate Structures for Enhanced Thermal Performance." Journal of Computer, Signal, and System Research 2, no. 2 (2025): 42–47. https://doi.org/10.71222/2xatmz14.

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The study presents a systematic approach to optimizing heat sink performance in high-heat flux applications through topology optimization (TO). A computational framework was developed that combines computational fluid dynamics (CFD) simulations with a simplified two-dimensional thermo-fluidic model to reduce computational complexity while maintaining accuracy. The design domain was constructed to minimize pressure drop under specific thermal constraints, with material properties interpolated using a rational approximation of material properties (RAMP) method to ensure a smooth transition between fluid and solid regions during optimization. Validation through three-dimensional numerical simulations in ANSYS Fluent confirmed the reliability of the two-dimensional model, with turbulence modeling and mesh refinement ensuring high accuracy in capturing critical flow and thermal characteristics. The results indicate that the topology-optimized designs achieved significant improvements over conventional straight-channel heat sinks, including a 25% reduction in thermal resistance and up to a 30% increase in heat transfer efficiency under varying flow rates. Moreover, the study demonstrates the feasibility of integrating artificial intelligence algorithms to streamline design optimization processes and enhance adaptability to complex performance requirements. These findings offer valuable insights for advancing heat management solutions in high-performance electronics and related applications.

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Jaradat, Ameera Saleh, and Mohammed Qassim Shatnawi. "Solving School Bus Routing Problem by Intelligent Water Drops Algorithm." Journal of Computer Science 16, no. 1 (2020): 25–34. http://dx.doi.org/10.3844/jcssp.2020.25.34.

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Camacho-Villalón, Christian Leonardo, Marco Dorigo, and Thomas Stützle. "The intelligent water drops algorithm: why it cannot be considered a novel algorithm." Swarm Intelligence 13, no. 3-4 (2019): 173–92. http://dx.doi.org/10.1007/s11721-019-00165-y.

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Thilagavathi, D., and Antony Selvadoss Thanamani. "Intelligent Water Drop Algorithm Based Particle Swarm Optimization (IWDPSO) Towards Multi Objective Job Scheduling for Grid Computing." Research Journal of Applied Sciences, Engineering and Technology 9, no. 11 (2015): 982–89. http://dx.doi.org/10.19026/rjaset.9.2591.

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Srivastava, Praveen Ranjan, Amitkumar Patel, Kunal Patel, and Prateek Vijaywargiya. "Test Data Generation Based on Test Path Discovery Using Intelligent Water Drop." International Journal of Applied Metaheuristic Computing 3, no. 2 (2012): 56–74. http://dx.doi.org/10.4018/jamc.2012040105.

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Automatic test data generation is required to generate test cases dynamically for a specific software program. Manual generation of test data is too tedious and a time consuming task. This paper proposes a technique using Intelligent Water Drop (IWD) for automatic generation of test data. Correctly generated test data helps in reducing the effort while testing the software. This paper discusses different algorithms based on IWD to generate test data and path coverage over Control Flow Graph. Test data is generated keeping in mind all of the programming constraints like “if,” “while,” “do while,” etc., available in the program.

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Alijla, Basem O., Li-Pei Wong, Chee Peng Lim, Ahamad Tajudin Khader, and Mohammed Azmi Al-Betar. "An ensemble of intelligent water drop algorithms and its application to optimization problems." Information Sciences 325 (December 2015): 175–89. http://dx.doi.org/10.1016/j.ins.2015.07.023.

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Setiawan, F., A. Sadiyoko, and C. Setiardjo. "Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem." International Journal of Industrial Engineering and Engineering Management 2, no. 1 (2020): 45–56. http://dx.doi.org/10.24002/ijieem.v2i1.3841.

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The multidimensional knapsack problem (MKP) is a generalization of the classical knapsack problem, a problem for allocating a resource by selecting a subset of objects that seek for the highest profit while satisfying the capacity of knapsack constraint. The MKP have many practical applications in different areas and classified as a NP-hard problem. An exact method like branch and bound and dynamic programming can solve the problem, but its time computation increases exponentially with the size of the problem. Whereas some approximation method has been developed to produce a near-optimal solution within reasonable computational times. In this paper a pigeon inspired optimization (PIO) is proposed for solving MKP. PIO is one of the metaheuristic algorithms that is classified in population-based swarm intelligent that is developed based on the behavior of the pigeon to find its home although it had gone far away from it home. In this paper, PIO implementation to solve MKP is applied to two different characteristic cases in total 10 cases. The result of the implementation of the two-best combination of parameter values for 10 cases compared to particle swarm optimization, intelligent water drop algorithm and the genetic algorithm gives satisfactory results.

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Nonita, S., Pardayev Abdunabi Xalikovich, C. Ramesh Kumar, et al. "Intelligent Water Drops Algorithm-Based Aggregation in Heterogeneous Wireless Sensor Network." Journal of Sensors 2022 (September 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/6099330.

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This paper provides a novel implementation of the intelligent water drops (IWD) method for resolving data aggregation issues in heterogeneous wireless sensor networks (WSN). When the aggregating node is utilized to transmit the data to the base station, the research attempts to show that the traffic situations of WSN may be modified appropriately by parameter tuning and algorithm modification. IWD is used to generate an optimum data aggregation tree in WSN as one of its applications. IWD assumes that all nodes in the environment are identical, resulting in identical parameter updates for all nodes. In practical scenarios, however, diverse nodes with variable beginning energy, communication range, and sensing range characteristics are deployed. In order to replicate the influence of heterogeneity in the environment, improved IID (IIWD) is offered as an enhancement to the original IID. The suggested enhancement is appropriate for scenarios in which the aggregation node is utilized to transmit data to the base station in heterogeneous configurations. In terms of residual energy, dead nodes, payload, and network lifespan, a series of simulation results demonstrates that the proposed IIWD significantly improves the accuracy and effectiveness of the IWD method in comparison.

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Salmanpour, Soheila, and Homayun Motameni. "Optimal path planning for mobile robot using Intelligent Water Drops algorithm." Journal of Intelligent & Fuzzy Systems 27, no. 3 (2014): 1519–31. http://dx.doi.org/10.3233/ifs-131118.

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