Intelligent Water Drops Research Articles

Discrete bacteria foraging optimization algorithm for graph based problems – a transition from continuous to discrete

ABSTRACTBacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.

Research on the cable routing optimisation by hybrid intelligent water drop algorithm

Research on the cable routing optimisation by hybrid intelligent water drop algorithm

Research on the cable routing optimisation by hybrid intelligent water drop algorithm

Cable assembly problem is formulated as searching for cable routes and the cable routing problem is one of the most challenging optimisation problems. Cable routing is the process of searching for obstacle-free path, which is constrained by the assembly points. Before routing, the routing environment is simplified to a two-dimensional grid map for computing reduction. In order to get the optimisation routing path, a novel approach of hybrid intelligent water drop algorithm (HIWDA) for cable routing is proposed, which is the hybridisation of particle swarm optimisation (PSO) and intelligent water drop algorithm (IWDA) for global searching and local searching respectively. In addition, B-spline curves are employed for smoothing routes obtained by HIWDA and making it more practical and more feasible for assembly. The result of HIWDA is verified by comparison with other algorithms in simulation on experiments.

REDUCTION OF ACTIVE POWER LOSS BY IMPROVED INTELLIGENT WATER DROP ALGORITHM

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.

Service composition in the multi cloud environment

PurposeUser requests over the cloud are not achievable with one single service, multiple services need to be executed to fulfill what a user asks for. Typically, such services are composed and presented as one global service. Moreover, the same operation can be achieved by multiple services available at different clouds, which can result in different possibilities in composing them. This paper aims to decrease the number of clouds involved in the composition process, so that user requests are satisfied with minimal cost (communication costs, execution time and financial charges).Design/methodology/approachThis paper investigates the use of an intelligent water drops (IWDs) optimization-based algorithm, and an integer linear programming model to optimize the number of cloud bases involved in the composition process. A comparison of the solutions found by these two techniques is presented in the paper.FindingsThe obtained results show that the number of cloud bases can be decreased without affecting user satisfaction.Originality/valueThe paper is a first attempt to use the IWDs algorithm for service composition, tested with big-size data.

A FEATURE SELECTION TECHNIQUE FOR INTRUSION DETECTION SYSTEM BASED ON IWD AND ACO

Rapid advancements in the internet technology and its vulnerabilities have led researchers to devise intelligent systems that can provide network security. Intrusion detection systems (IDS) scrutinize all the features to detect intrusive data. Some of the features may be redundant or irrelevant to the detection process, which results in computational complexities and increased training time. To mitigate this problem, a process known as feature selection is used to remove the redundant and irrelevant attributes of the dataset. This paper proposes an intelligent hybrid technique for the purpose of feature selection of KDD CUP’99 dataset using the concepts of metaheuristic optimization. This hybrid approach combines the concepts of Intelligent Water Drops(IWD) and Ant Colony Optimization(ACO)to select features from data. The objective of this work is to optimize the process of feature selection in order to achieve optimal feature subset and reduce the training time.

Novel localization algorithm for wireless sensor network based on intelligent water drops

High localization rigor and low development expense are the keys and pivotal issues in operation and management of wireless sensor network. This paper proposes a neoteric and high efficiency algorithm which is based on new optimization method for locating nodes in an outdoor environment. This new optimization method is non-linear optimization method and is called intelligent water drops (IWDs). It is proposed that the objective function which need to be optimized by using IWDs is the mean squared range error of all neighboring anchor nodes. This paper affirms that received signal strength indicator (RSSI) is used to determine the interior distances between WSNs nodes. IWDs is an elevated performance stochastic global optimization tool that affirms the minimization of objective function, without being trapped into local optima. The proposed algorithm based on IWDs is more attractive to promote elevated localization precision because of a special features that is an easy implementation of IWDs, in addition to non cost of RSSI. Simulation results have approved that the proposed algorithm able to perform better than that of other algorithms based on optimization techniques such as ant colony, genetic algorithm, and particle swarm optimization. This is distinctly appear in some of the evaluation metrics such as localization accuracy and localization rate.

Supply chain design using a modified IWD algorithm

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.

An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

Cloud computing is emerging as a high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. Many resource management methods may enhance the efficiency of the whole cloud computing system. The key part of cloud computing resource management is resource scheduling. Optimized scheduling of tasks on the cloud virtual machines is an NP-hard problem and many algorithms have been presented to solve it. The variations among these schedulers are due to the fact that the scheduling strategies of the schedulers are adapted to the changing environment and the types of tasks. The focus of this paper is on workflows scheduling in cloud computing, which is gaining a lot of attention recently because workflows have emerged as a paradigm to represent complex computing problems. We proposed a novel algorithm extending the natural-based Intelligent Water Drops (IWD) algorithm that optimizes the scheduling of workflows on the cloud. The proposed algorithm is implemented and embedded within the workflows simulation toolkit and tested in different simulated cloud environments with different cost models. Our algorithm showed noticeable enhancements over the classical workflow scheduling algorithms. We made a comparison between the proposed IWD-based algorithm with other well-known scheduling algorithms, including MIN-MIN, MAX-MIN, Round Robin, FCFS, and MCT, PSO and C-PSO, where the proposed algorithm presented noticeable enhancements in the performance and cost in most situations.

An IWD-based feature selection method for intrusion detection system

Intrusion detection system (IDS) is an essential cyber security tool which is used to detect abnormal activity on a network or a host. A general approach towards designing IDS models is to use classifiers as detection units. But a large feature space including noisy, redundant and irrelevant features often leads to low detection and high misclassification rates by the classifier. To address this drawback, the process of selecting most relevant key features for classification is highly important. The objective of this work is to optimize the process of feature selection in a way that improves the accuracy of the classifier. This paper presents an IDS model wherein an intelligent water drops (IWD) algorithm-based feature selection method is proposed. This method uses the IWD algorithm, a nature-inspired optimization algorithm for the feature subset selection along with support vector machine as a classifier for evaluation of the features selected. The experiments are conducted using KDD CUP’99 dataset, and the performance is compared with earlier designs. The experimental results show that the proposed model performs better in terms of higher detection rate, low false alarm rate and improved accuracy than the existing approaches.

Bi-objective intelligent water drops algorithm to a practical multi-echelon supply chain optimization problem

Industrial Clusters (ICs) are defined as geographically adjacent interconnected companies working together within the same commercial section which enjoy unusual competitive success in their field. The relationships between Supply Chain Management (SCM) and ICs have been imperfectly mathematically investigated, in spite of their intrinsic correlation. To bridge this gap, a bi-objective multi-echelon supply-distribution model is firstly proposed in this paper to optimize collaborations of different echelons. The considered problem is then solved using a recently introduced metaheuristic algorithm, the intelligent water drops (IWD) algorithm in terms of a multi-objective approach. The IWD is then compared with two well-known algorithms, reference-point based non-dominated sorting genetic algorithm (NSGA-III) and non-dominated ranking genetic algorithm (NRGA). The two considered objectives are 1) minimizing the total incurred logistics costs, and 2) maximizing the service level of customers. The small-and-medium sized enterprises (SMEs) positioned in IC as manufacturers profit from using a 3PL-managed supply-demand hub in industrial cluster (SDHIC) as a public provider of warehousing and logistics services. The validity of the proposed approach is illustrated through experimental results including comprehensive statistical analysis on the three used measurement metrics.

A Research of Cold-chain Logistic Distribution Path Optimization Based on the Improved Intelligent Water Drop Algorithm

A Research of Cold-chain Logistic Distribution Path Optimization Based on the Improved Intelligent Water Drop Algorithm

Strategic design for dynamic multi-zone truckload shipments: A study of OTOP agricultural products in Thailand

The one-tambon-one-product (OTOP) project in Thailand has been successful according to statistical data from the Interior Ministry's Department of Community Development. However, there is a concern that dispatchers in the supporting trucking industry carry out both tasks of truckload assignment and driver scheduling. It is necessary to investigate more efficient and reliable alternatives to handle the growing and fluctuating quantities of inbound and outbound goods in each service area. This paper focuses on determining alternative ways for enhancing the efficiency of the transportation management system based on multi-zone dispatching (MZD) with the objective of minimising total load imbalances. This problem is considered to be a special case of the MZD but it is complex when compared to the conventional MZD, especially since planning horizons are considered and modified as a dynamic MZD (dMZD). In this paper an intelligent water drop optimisation (IWD) was used to solve the problem. In order to improve the solution quality, the novel two-phase approximated algorithm (2-PAA) with two additional processes from the variable neighbourhood and dynamic programming algorithms, were proposed. Experimental results with preferable settings showed the 2-PAA was computationally effective and feasible to generate dispatching alternatives for both static and dynamic problems. Using a set of real data, the simulation suggested a 3-period dMZD configuration for driver scheduling with preferable levels of tour length and imbalance statistics.

Intelligent water drops based joint subcarrier pairing and power allocation with fairness in cooperative relay networks

In this paper, we propose a joint subcarrier pairing and power allocation (JS2PA) scheme with fairness based on the intelligent water drop (IWD) optimization method for orthogonal frequency division multiple access cooperative relay networks. The proposed scheme consists of a subcarrier pairing and selection al- gorithm and a power allocation algorithm. We first formulate the JS2PA problem as a mixed integer programming problem aiming to maximize the total network utility under the constraints of in- dividual power, subcarrier fairness requirement and pairing. To solve the non-convex JS2PA problem, firstly, we propose a subcar- rier pairing and selection algorithm based on Hungarian method so as to select the appropriate subcarrier pairs for relaying. Sec- ondly, we provide a power allocation algorithm based on the IWD method in which water drops act as the agents to find the optimal power allocation for each node. In particular, to improve the opti- mality of power allocation, we integrate network utility and power requirement into heuristic function to measure the desirability of the IWD selecting the next visiting node. Finally, we conduct sim- ulations to validate the proposed algorithms and the results show that the proposed JS2PA scheme outperforms the existing methods in terms of convergence, total network utility and fairness.

Community detection using intelligent water drops optimisation algorithm

Community structure means the existence of densely connected subgroups in the networks. It is a surprising property that appears in complex and naturally constructed networks. We are proposing a novel heuristic approach to the community detection problem. In this paper, the community detection problem is solved using the intelligent water drop heuristic on a group of real life networks. The proposed heuristic succeeded in grouping the nodes in the network into sets of densely connected subgroups. Our approach uses the modularity value as an optimisation criterion. The quality of the resulting division in the network was proven using measures like modularity and NMI. The experimental results verify that our algorithm is highly efficient at discovering quality community structure.

B-Spline Curve Fitting with Intelligent Water Drops (IWD)

The use of B-spline curves has spreaded too many fields such as computer aided design (CAD), data visualization, surface modelling, signal processing and statistics. The flexible and powerful mathematical properties of B-spline are the cause of being one of the most preferred curve in literature. They can represent a large variety of shapes efficiently. The curve behind of the model can be obtained by doing approximation of control points, approximation of knot points or parameterization. It is obvious that the selection of knot points in B-spline curve approximation has an important and considerable effect on the behaviour of final approximation. In addition to this, an unreasonable knot vector may introduce unpredictable and unacceptable shape. Recently, in literature, there has been a considerable attention on the algorithms inspired from natural processes or events to solve optimization problems such as simulated annealing, ant colony optimization, particle swarm optimization, artificial bee colony optimization, and genetic algorithms. This paper implements and analyses a solution to approximate B-spline curves using Intelligent Water Drops (IWD) algorithm. This algorithm is a swarm based optimization algorithm inspired from the processes that happen in the natural river systems. The algorithm is based on the actions and reactions that take place between water drops in the river and the changes that happen in the environment. Some basic properties of natural water drops are adopted in the algorithm here to solve B-spline curve fitting problem. Optimal knots are selected through IWD algorithm. The proposed algorithm convergences optimal solutions and finds good and promising results.

IWDRP: An Intelligent Water Drops Inspired Routing Protocol for Mobile Ad Hoc Networks

This paper deals with the problem of routing in Mobile Ad hoc Networks (MANET). A mobile ad hoc network is a collection of mobile devices deployed without any pre-established infrastructure or centralized administration. Routing is a very challenging issue since the appearance of this technology. The main goal of every routing protocol is to find a route between two communicating nodes while optimizing overall performances of the network. This paper introduces a novel routing protocol inspired from the nature and that should deal with the dynamic aspect of MANET. The used approach, called Intelligent Water Drops (IWD), mimics the processes that happen in the natural river systems, particularly, the actions that water drops perform in the rivers to find the shortest path to their destination (sea). In fact, it is observed that water drops of a river often find good paths among lots of possible paths in their ways from a source to a destination. We combined these ideas with a route failure prediction mechanism to develop a new routing protocol for MANETs called IWDRP. This prediction method is based on the received signal strength indicator. Further simulation results show that IWDRP is able to achieve better results in terms of packet delivery, end-to-end delay in comparison with AODV-BFABL. The achievement in this paper has certain reference value to the further study of the routing issue in MANETs.

A multi-objective intelligent water drop algorithm to minimise cost Of goods sold and time to market in logistics networks

The Intelligent Water Drop (IWD) algorithm is inspired by the movement of natural water drops (WD) in a river. A stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a sea. In the process of reaching such destination, the WD and the environment interact with each other as the WD moves through the river bed. Similarly, the supply chain problem can be modelled as a flow of stages that must be completed and optimised to obtain a finished product that is delivered to the end user. Every stage may have one or more options to be satisfied such as suppliers, manufacturing or delivery options. Each option is characterised by its time and cost. Within this context, multi–objective optimisation approaches are particularly well suited to provide optimal solutions. This problem has been classified as NP hard; thus, this paper proposes an approach aiming to solve the logistics network problem using a modified multi–objective extension of the IWD which returns a Pareto set.Artificial WD, flowing through the supply chain, will simultaneously minimise the cost of goods sold and the lead time of every product involved by using the concept of Pareto optimality. The proposed approach has been tested over instances widely used in literature yielding promising results which are supported by the performance measurements taken by comparison to the ant colony meta-heuristic as well as the true fronts obtained by exhaustive enumeration. The Pareto set returned by IWD is computed in 4 s and the generational distance, spacing, and hyper–area metrics are very close to those computed by exhaustive enumeration. Therefore, our main contribution is the design of a new algorithm that overcomes the algorithm proposed by Moncayo-Martínez and Zhang (2011).This paper contributes to enhance the current body of knowledge of expert and intelligent systems by providing a new, effective and efficient IWD-based optimisation method for the design and configuration of supply chain and logistics networks taking into account multiple objectives simultaneously.

Composition of Optimized Assessment Sheet with Multi-criteria using Evolutionary IntelligentWater Drops (EvIWD) Algorithm

At the present time, computerized tests are one of the most critical means to evaluate learning. Choosing tailored questions for each learner is a important part of such tests. Since, wide and varied learners with different abilities are involved, even randomized test cannot serve the purpose of assessment. Some form of personalized and intelligent testing is needed in E-Learning. One of the main components in composing intelligent testing is selecting the items from a huge Item Bank as the accuracy of the test depends on the quality of the assessment which in turn depends on the items selected for assessment. Furthermore, pickingappropriate items is critical in developing as assessment sheet that satisfies multiple criteria. It includes the number of test items, the definitedissemination of course concepts to be assessed, and the expected degree of difficultness and discrimination and exposure frequency. These tests, must effectively select questions from a large item bank, and to manage this problem an optimized assessment sheet composition system using the modified form of nature inspired Intelligent Water Drops optimization algorithm is proposed by embedding a local heuristic as evolutionary operator. This system is designed to choosepersonalizedtest items for each and every learner. Furthermore, the proposed approach is able to effectively generate near optimal items from large item bank that satisfy multiple constraints. The results show that the Evolutionary Intelligent Water Drops approach is suitable for the selection of nearoptimal items from large-scale item bank.

Cloud Computing Intelligent Management by Metaheuristic Algorithm of Intelligent Water Drop

2 Abstract: One of the most important challenges in Cloud Network is the matter of allocating physical resources to virtual machines. The goal of this paper is to present a method to have an optimized installation of virtual resources onto physical machines with intelligent water drop so that overall energy consumption in data center is reduced with decreasing the number of active physical servers. Utilizing policies to host overload detection and immigrant virtual machine; and through comparing results of simulation with existing methods; is the best method to manage physical resources in cloud networks which for this purpose, intelligent water drop algorithm has been used. Also to simulate cloud environment, Cloudsim 3.0 simulator library in Netbeans IDE 7.4 develop environment are going to be used. Results shows that in comparison with other methods, the algorithm of intelligent water drop using MAD algorithms as host overload detection algorithm and MMT as the algorithm of selecting virtual machine, the most optimized answer will be found for physical resource management in cloud computing.