Modified and Ensemble Intelligent Water Drop Algorithms and Their Applications

1.1 Introduction Optimization is a process that concerns with finding the best solution of a given problem from among the possible solutions within an affordable time and cost (Weise et al., 2009). The first step in the optimization process is formulating the optimization problem through an objective function and a set of constrains that encompass the problem search space (ie, regions of feasible solutions). Every alternative (ie, solution) is represented by a set of decision variables. Each decision variable has a domain, which is a representation of the set of all possible values that the decision variable can take. The second step in optimization starts by utilizing an optimization method (ie, search method) to find the best candidate solutions. Candidate solution has a configuration of decision variables that satisfies the set of problem constrains, and that maximizes or minimizes the objective function (Boussaid et al., 2013). It converges to the optimal solution (ie, local or global optimal solution) by reaching the optimal values of the decision variables. Figure 1.1 depicts a 3D-fitness landscape of an optimization problem. It shows the concept of the local and global optima, where the local optimal solution is not necessarily the same as the global one (Weise et al., 2009). Optimization can be applied to many real-world problems in various domains. As an example, mathematicians apply optimization methods to identify the best outcome pertaining to some mathematical functions within a range of variables (Vesterstrom and Thomsen, 2004). In the presence of conflicting criteria, engineers use optimization methods t

Oiahcr: online isolated arabic handwritten character recognition using neural network.

In this paper, an online isolated Arabic handwritten character recognition system is introduced. The system can be adapted to achieve the demands of hand-held and digital tablet applications. To achieve this goal, despite of single neural networks, four neural networks are used, one for each cluster of characters. Feed forward back propagation neural networks are used in classification process. This approach is employed as classifiers due to the low computation overhead during training and recall process. The system recognizes on-line isolated Arabic character and achieves an accuracy rate 9٥. 7% from untrained writers and 99.1% for trained writers

Modified And Ensemble Intelligent Water Drop Algorithms And Their Applications

Algoritma Titisan Air Cerdas (TAC) ialah model berasaskan kawanan yang sememangnya berguna untuk mengatasi masalah-masalah pengoptimuman. Tujuan utama kajian ini adalah untuk meningkatkan keupayaan algoritma TAC dan mengatasi keterbatasan algoritma tersebut, yang berkaitan dengan kepelbagaian populasi serta mengimbangangi penerokaan dan pengeksploitasian dalam menangani masalah-masalah pengoptimuman. Pertama, algoritma TAC yang diubahsuai, diperkenalkan. The Intelligent Water Drop (IWD) algorithm is a swarm-based model that is useful for undertaking optimization problems. The main aim of this research is to enhance the IWD algorithm and overcome its limitations pertaining to population diversity, as well as balanced exploration and exploitation in handling optimization problems

An ensemble of intelligent water drop algorithm for feature selection optimization problem

Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems

ويكي دوك ألينار: اداة من على الرف لمحاذات مستندات ويكيبيديا

Wikipedia encyclopedia is an attractive source for comparable corpora in many languages. Most researchers develop their own script to perform document alignment task, which requires efforts and time. In this paper, we present WikiDocsAligner, an off-the-shelf Wikipedia Articles alignment handy tool. The implementation of WikiDocsAligner does not require the researchers to import/export of interlanguage links databases. The user just need to download Wikipedia dumps (interlanguage links and articles), then provide them to the tool, which performs the alignment. This software can be used easily to align Wikipedia documents in any language pair. Finally, we use WikiDocsAligner to align comparable documents from Arabic Wikipedia and Egyptian Wikipedia. So we shed the light on Wikipedia as a source of Arabic dialects language resources. The produced resources is interesting and useful as the demand on Arabic/dialects language resources increased in the last decade.لا يوج

Neural network-based minutiae extraction for fingerprint verification system

Fingerprint is one of the most important biometrics that has been employed for verification systems. Fingerprint is characterized by two fundamental properties; Easy to acquire, and it is unique for each person. This paper presents minutia extraction method based on Neural Network-based. These features can be used in verification systems. The verification process includes four main phases: image acquisition, preprocessing, feature extraction, and pattern matching. The method is applied on a set of fingerprint images and the results show that the average matching accuracy of fingerprints is 91.6%

Flow-Based IDS for ICMPv6-Based DDoS Attacks Detection

The Internet Control Message Protocol version Six (ICMPv6) is categorized as the most important part of the Internet Protocol version Six (IPv6) due to its core functionalities. However, ICMPv6 protocol is vulnerable to different types of attacks such as Distributed Denial of Services (DDoS) attacks that are based on ICMPv6 messages. ICMPv6-based DDoS attacks are the most performed attacks against IPv6 networks and considered a grave problem of today Internet. Intrusion Detection Systems (IDSs) under different categories have been proposed to detect ICMPv6-based DDoS attacks. However, these IDSs are inefficient in detecting the attacks due to their limitations. The main limitation of the existing IDSs is the dependency on packet-based representation and features which are unsuitable for detecting DDoS attacks as experimentally proven. Therefore, this research proposes a new IDS, based on a flow

Intelligent water drops algorithm for rough set feature selection

In this article; Intelligent Water Drops (IWD) algorithm is adapted for feature selection with Rough Set (RS). Specifically, IWD is used to search for a subset of features based on RS dependency as an evaluation function. The resulting system, called IWDRSFS (Intelligent Water Drops for Rough Set Feature Selection), is evaluated with six benchmark data sets. The performance of IWDRSFS are analysed and compared with those from other methods in the literature. The outcomes indicate that IWDRSFS is able to provide competitive and comparable results. In summary, this study shows that IWD is a useful method for undertaking feature selection problems with RS

β-hill climbing algorithm for Sudoku game

In this paper, β-Hill Climbing algorithm, the recent local search-based meta-heuristic, are tailored for Sudoku puzzle. β-Hill Climbing algorithm is a new extended version of hill climbing algorithm which has the capability to escape the local optima using a stochastic operator called β-operator. The Sudoku puzzle is a popular game formulated as an optimization problem to come up with exact solution. Some Sudoku puzzle examples have been applied for evaluation process. The parameters of the β-Hill Climbing is also studied to show the best configuration used for this game. β-Hill Climbing in its best parameter configuration is able to find solution for Sudoku puzzle in 19 iterations and 2 seconds

An ensemble of intelligent water drop algorithms and its application to optimization problems

Crown Copyright © 2015 Published by Elsevier Inc. All rights reserved. The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems