Intelligent Water Drops Algorithm Research Articles

Brain Image Segmentation to Diagnose Tumor by Applying Wiener Filter and Intelligent Water Drop Algorithm

Brain Image Segmentation to Diagnose Tumor by Applying Wiener Filter and Intelligent Water Drop Algorithm

Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm's performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.

An extended intelligent water drop approach for efficient VM allocation in secure cloud computing framework

Cloud computing provides many advantages services to its users like online resources accessibility, better cost management, dynamic resource pooling, and efficient Virtual Machine (VM) allocation. With so many useful services, cloud computing also introduces several security risks of cloud user information privacy as well as select the minimum number of VM to execute task load and improve resource utilization are some challenging issues. The objective of this work is to achieve higher resource utilization rate while considering the security of the cloud data. We extended the natural phenomena based Intelligent Water Drop (IWD) algorithm and proposed a VM allocation algorithm which optimizes the task execution in a secure cloud environment. The proposed work is implemented on the CloudSim simulation toolkit, and to test the validity of the algorithm, a comparison is performed with other well-known VM allocation policies in the cloud computing. The experimental simulation results showed that the proposed VM allocation policy performed better than exiting VM allocation approaches.

Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach

Cloud computing is undoubtedly one of the most significant advances in the domain of information technology. It facilitates elastic and on-demand provisioning of high performance computing capabilities employing pay-per-use model that has snowballed its adoption by scientists and engineers over the past few years. They often exploit workflows to represent their massive applications. Workflow scheduling in cloud has been devoted considerable investigation by researchers owing to its NP-complete nature of problem. Most of the previous studies targeted optimization of schedule length and execution cost within given deadlines/budget restrictions, or both. However, enormous energy consumption in the cloud data centers is not only negatively impacting the environment but also resulting in increased operational costs and thus cannot be ignored. Efficient scheduling strategies can significantly lessen the energy usage while complying with the user’s Quality of Service limitations. This research study proposes a Hybrid Approach for Energy aware scheduling of Deadline constrained workflows (HAED) using Intelligent Water Drops algorithm and Genetic Algorithm, which provides non-dominated solutions to the user. In particular, it focuses on multiple objectives i.e. reduction of schedule length, execution cost and energy usage within deadlines specified by the user. Its performance has been assessed on four scientific workflows from diverse domains using hypervolume and set coverage. The results achieved with the simulations demonstrate that the solutions produced by HAED are of better quality in terms of accuracy and diversity than non-dominated sorting genetic algorithm and hybrid particle swarm optimization.

A multi-objective meta-heuristic approach for the transit network design and frequency setting problem

ABSTRACT The Transit Network Design and Frequency Setting Problem (TNDFSP) can be defined as the creation of effective routes in a public transport network and the determination of relevant frequencies. Generally, the TNDFSP problem is in the same category as the Traveling Salesman Problem (TSP), which is known to be a non-deterministic polynomial-period (NP-hard) difficult problem. This study consists of two stages: first, the design of a public transport network with an evolutionary optimization technique – the Intelligent Water Drops (IWD) algorithm – based on the TSP and the determination of relevant frequencies; and second, the assignment of passengers to designated routes. All decisions related to public transport network design may be evaluated by considering environmental costs in relation to passengers, operators and the environment. This study presents an acceptable, constructive and original algorithm.

An efficient spectral balance method for transient analysis of single phase overhead lines in the presence of surge arresters

AbstractIn this paper, an efficient hybrid method is proposed for transient analysis of an overhead line equipped with a grounded arrester. The proposed method adopts a nonlinear equivalent circuit consisting of linear and nonlinear parts respectively associated with the overhead line and the arrester. In the first part of the paper, an intelligent model based on qualitative concepts in electromagnetics is proposed for derivation of the linear part. With the use of this qualitative model, the behavior of the linear part is first extracted as simple and unchanged membership functions, and effects of parameters of the problem, that is, the height of the overhead line and the electrical parameters of the ground, are then extracted as simple curves so that the linear part based on the qualitative model is efficiently computed in the frequency domain. The qualitative model, in contrast with the conventional numerical methods, is very efficient, and in comparison with the approximate ones, is broad band. In the second part of the paper, with the aid of an efficient optimization algorithm, called intelligent water drop algorithm (IWD), the lightning overvoltage across the arrester is efficiently computed.

Dynamic Vehicle Distribution Path Optimization Based on Improved Intelligent Water Drop Algorithm

Dynamic Vehicle Distribution Path Optimization Based on Improved Intelligent Water Drop Algorithm

Augmented intelligent water drops optimisation model for virtual machine placement in cloud environment

Virtual machine placement in cloud computing is to allocate the virtual machines (VMs) (user request) to suitable physical machines (PMs) so that the wastage of resources is reduced. Allocation of appropriate VMs to suitable and effective PMs will lead the service provider to be a better competitor with more available resources for affording a greater number of VMs simultaneously which in turn reflects with the growth in the economy. In this research work, an augmented intelligent water drop (IWD) algorithm is used for effectively placing VMs. The preliminary goal of this proposed work is to reduce the overall resource utilisation by packing the VMs to appropriate PMs effectively. The proposed IWD model is tested under the standard simulation process as it is given in the literature. Performance of IWD is compared with the existing techniques first fit decreasing, least loaded and ant colony optimisation algorithm. Performance analysis shows the significance of the proposed method over existing techniques.

Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

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.

An optimization of distributed Voronoi-based collaboration for energy-efficient geographic routing in wireless sensor networks

In energy-constrained wireless sensor networks (WSNs), geographic routing (GR) also known as location-aware routing has been developed because it uses neighbourhood locality data as an alternative of overall topology information for routing. But, this protocol frequently suffers from the energy holes in the data transmission resulting in path failure. To avoid this problem, energy-aware dual-path GR (EDGR) protocol has been suggested to improve the routing path from energy holes. However, it is unable to heal the energy holes since the node mobility can generate new energy holes. Also, higher mobility distance can cause high energy consumption (EC) and node failure. Hence in this article, an energy-efficient distributed collaboration mechanism is proposed with EDGR for $$k$$ -coverage energy holes detection and healing in WSN that minimizes the delivery delay (DD). In this protocol, the distributed Voronoi-based collaboration (DVOC) method is applied in which the nodes can cooperate in energy holes detection and recovery. The nodes are enabled to monitor each other node’s critical locations around themselves by constructing the local Voronoi diagrams (LVDs). Moreover, an optimized DVOC (ODVOC) is proposed with EDGR in which intelligent water drop (IWD) algorithm is used to find the globally optimized routes to minimize the DD. Finally, the simulation outcomes demonstrate the ODVOC-EDGR accomplishes higher effectiveness than the EDGR and DVOC-EDGR in terms of different performance metrics.

Quality of service enabled intelligent water drop algorithm based routing protocol for dynamic link failure detection in wireless sensor network

Quality of service enabled intelligent water drop algorithm based routing protocol for dynamic link failure detection in wireless sensor network

Intelligent Water Drop Algorithm based Relevant Image Fetching using Histogram and Annotation Features

Social media network increase trend of image collection at various platforms. Hence getting an relevant image as per query image or text is depend on retrieval algorithms. Number of researcher has proposed algorithms for fetching relevant images, but relevancy of those still need improvement. Hence proposed paper has utilized the Intelligent water Drop algorithm for initial clustering of images as per feature values. Clustering or relevancy of an image depends on visual feature histogram and annotation similarity. Here property of moving a water drop from one node to another in a water drop graph has increase the clustering accuracy of the work. Experiment was done on real dataset having five different group of image set with annotation. Result shows that proposed work has increase the retrieval relevancy accuracy as well as reduce the fetching of the images. This reduction of time was obtained by using the clustering structure of image dataset.

Neural network‐based routing protocol for opportunistic networks with intelligent water drop optimization

SummaryOpportunistic Networks (OppNets) is a system of wirelessly connected nodes in a varying network topology. Routing in OppNets is a challenge. To overcome the problem of routing, an intelligent dynamic strategy to select next best node for forwarding a message is required. This paper proposes an intelligent routing mechanism based on Intelligent Water Drop (IWD) Algorithm which is used in tandem with Neural Networks (NNs) as an optimization technique to solve the problem of routing in such networks. The nature–inspired IWD algorithm provides robustness, whereas the neural network base of the algorithm helps it to make intelligent routing decisions. The weights in the Neural Network model are calculated by IWD Algorithm using training data consisting of inputs that are characteristic parameters of nodes, such as buffer space, number of successful deliveries and energy levels along with transitive parameters such as delivery probabilities. The proposed protocol Intelligent Water Drop Neural Network (IWDNN) is compared with other protocols that use similar ideologies such as MLProph, K‐nearest neighbour classification based routing protocol (KNNR), Cognitive Routing Protocol for Opportunistic Network (CRPO), and Inheritance Inspired Context Aware Routing Protocol (IICAR), as well as the standard protocol Prophet. IWDNN is shown to outperform all other protocols with an average message delivery ratio of 60%, which is a significant improvement of over 10% in comparison to other similarly conceived algorithms. It has one of the lowest latency among the protocols studied, in a range of 3000 to 4000 s, and incurs comparably low overhead costs in the range of 15 to 30. The drop ratios are one of the lowest, staying near six and approaching zero as buffer size is increased. Average amount of time a message stayed in the buffer was the lowest, with a mean of 1600 s.

Scheduling scientific workflows on virtual machines using a Pareto and hypervolume based black hole optimization algorithm

The problem of workflow scheduling on virtual machines in a cloud environment is to find the near optimal permutation of the assignment of independent computational jobs on a set of machines that satisfies conflicting objectives. This problem is known to be an NP-hard problem. Evolutionary multiobjective algorithms are optimization methods to solve such problems. hypervolume is one of the most important criteria that is used to both as solution evaluation and as a guidance for near-optimal selection of a set of solutions called the Pareto front. In this paper, a new hypervolume-based multiobjective algorithm is proposed for driving the Pareto front. To this end, we extend the single-objective Black hole heuristic algorithm based on the adopted $$\theta $$ -dominance relation to improving the diversity and convergence to an optimal Pareto front. The conflicting objectives are resource utilization, resource cost, and the workflow makespan (completion time). Also to presenting the appropriate scheduling algorithm, we have proven the correctness of the proposed algorithm by providing the behavioral model of the suggested system using model checking tool. For this purpose, we first introduce the behavioral model of the proposed system using Abstract state machine and extract the properties of the algorithm in the form of linear temporal logic. Then we encode the algorithm using the model checker tool Abstract state Machine Meta-model-based Language and verify the accuracy of the algorithm based on the expected properties, reachability, fairness, and deadlock-free. In order to demonstrate the effectiveness of our method we: (1) extended the WorkflowSim tools, (2) applied it to both balanced and imbalanced workflows and (3) compared results to algorithms, Strength Pareto Evolutionary Algorithm-2, Non-dominated Sorting Genetic Algorithm-2, Multi-Swarm MultiObjective Optimization, Intelligent Water Drops algorithm and Genetic Algorithm and Pareto-based Grey Wolf Optimizer. The comparisons show that by increasing the number of users requests and their correlations, the proposed algorithm can find more optimal Pareto front.

Application of intelligent water drop algorithm in process planning optimization

Computer-aided process planning is an important part of computer integrated manufacturing, which is associated with computer-aided manufacturing and computer-aided design. This paper presents a process planning method based on the Intelligent Water Drop (IWD) algorithm. First, the operation units were defined by analyzing the machining features and analyzing the constraint relationship according to the processing characteristics of the operation unit as a constraint for process optimization. Then, the IWD algorithm was combined with the process planning problem. According to the iterative characteristics of the algorithm, the nodes in the flow path of the water droplets were associated with the operation units, and the amount of soil in the water droplets was correlated with the production cost. The mathematical model of process planning was constructed on the premise of analyzing the optimization objectives and machining constraints. By searching the feasible solution space limited by the sequential constraint matrix, the process planning scheme with the least production cost was obtained. Finally, an experiment of two prismatic parts was used to illustrate the application process of the IWD algorithm in process planning, and the calculation results were compared with other methods. The comparison results showed that the proposed method is feasible and effective in process planning.

Green Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center Based on Improved Intelligent Water Drop Algorithms

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.

Solving School Bus Routing Problem by Intelligent Water Drops Algorithm

The School Bus Routing Problem (SBRP) is an essential subject in the development of transportation. Solving the problem will have a great impact on the transportation systems by improving the quality of the provided service and reducing the operations costs. SBRP tries to find an efficient school buses plan where each bus takes up students from different bus stops and transports them to their schools while fulfilling number constraints including the maximum bus capacity and the time window of a school. This study used Intelligent Water Drops (IWD) optimization approach to resolve (SBRP). IWD is a meta-heuristic swarm-based optimization technique that simulates natural water drops. The scheme of the IWD algorithm is applied here to find a reasonable solution to SBRP. The application of IWD algorithm produces satisfying results within a reasonable amount of time.

Plug in electric vehicle based multi-area renewable energy system for Automatic Generation Control

The work presented proposes a combination of a quick and reliable controller for the 2-area thermal system with the plug-in electric vehicle. For controlling and normalizing the system output, controlling the rotational frequency of the generator is an effective technique. A multi-constrained function is effective but the new objective function formulated is further effective and has been used in the optimization process with Intelligent Water Drops (IWD) algorithm for tuning the controllers. The performance with nPID controller in the proposed system has been compared to structures such as PID and PI for validation of the effectiveness with step and dynamic load variation. Statistical analysis such as Analysis-of-variance (ANOVA) is also performed. The work is extended by integrating wind turbines and tested with a variation of 10% in step loading and ±5% in dynamic loading of 120/80 seconds for either area. A significant improvement in response time is witnessed.

Balancing Exploration-Exploitation in the Set Covering Problem Resolution with a Self-adaptive Intelligent Water Drops Algorithm

Balancing Exploration-Exploitation in the Set Covering Problem Resolution with a Self-adaptive Intelligent Water Drops Algorithm

Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center under Green Shipbuilding Mode

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.