A wireless mesh networks (WMNs) has emerged recently, WMN consist of two types of nodes mesh routers and mesh clients.WMN is dynamically self-organized and self-configured, with the nodes in the network automatically establishing and maintaining mesh connectivity among themselves. Channel assignment is a critical issue in WMN which is the mapping between the available channels and the radios at each node such that the network performance is optimized, and hence the focus of this work along with static channel assignment, multi-radio, multi-channel. Multi-radio MAC can potentially achieve higher network capacity than single-radio MAC. We design and implement a new agent (DSBCA) distributed channel assignment spanner base, the routing protocol Optimize the network performance considering the throughput, end-to-end delay, connectivity and the interference of our channel assignment algorithm. Base on the network simulations NS2.34 on Linux Fedora and our proposed channel assignment algorithm improved the throughput, minimal-connectivity preserving channel assignment algorithm.
The focus area of this paper is on the assignment problem with budget constraints which is one of the application area of combinatorial optimization that operates on the domain of those optimization problems,in which the set of feasible solutions is discrete or can be reduced to discrete,and in which the goal is to find the best solution. It is particularly concerned with solving the unconstrained assignment problems with Hungarian algorithm and the constrained assignment problem by cutting plane or outer linearization algorithm for solving the Lagrangian dual problem in which, at each iteration,the function that approximates the dual function is optimized. The paper is divided in to two chapters. In the first chapter,the classical assignment problem,the problem of finding optimum (minimum or maximum) cost or profit assignment a set of workers or resources to jobs or activities to gather with its mathematical formulation,solution methods and special cases in assignment problems were considered. Under the second chapter, we have considered generalization of the classical assignment problem concerning resource(or budget) constraints, due to the variety of real life problems.
In this project, we have proposed an efficient re-routing algorithm by dynamic routing in Bi-directional WDM optical network. In wavelength division multiplexing network, a heuristic algorithm is used for routing and wavelength assignment. The main objective of this is to minimize the requirement of wavelength and hop length between S-D nodes in the traffic. In this project we have to considering a wavelength routed WDM optical network and then implementing Heuristic algorithm on it. We have to divide our work mainly into two phases. In the first phase, existing routing is performed using Dijkstra’s algorithm and in second phase of algorithm proposed algorithm is performed to reduce the number of wavelengths required in the first phase of the network which minimizes the hop count of each route.
Assignment Problem is one of the first fundamental problems in the area of combinatorial optimization. Despite its historical roots, the problem has tremendous importance to date, due to its numerous real life applications in like facility location, personnel scheduling, job scheduling, production planning, project assignment, task assignment, time-tabling, vehicle routing, storage space allocation, etc. This book comprises some real life assignment and related problems involving imprecise cost/time parameters. In order to represent those imprecise parameters, interval numbers have been used here as this representation is the best among others. The associated problems have been solved using Genetic Algorithm efficiently. A salient feature of this book is that most of the algorithms have been written in a lucid manner with suitable examples using figures. This book should interest academicians, OR/GA practitioners and executives of different industries and business organizations.
In this book we consider the Frequency Assignment Problem, where the objective is to minimize the cost due to interference arising in a solution. We use a quadratic 0-1 integer programming formulation of the problem as a basis to derive new lower bounds and problem reduction rules. A tree search algorithm that uses the lower bounds and dominance criteria is also presented. Computational results are shown on standard benchmark instances from the literature.
A large number of wrappers generate tables without column names for human consumption because the meaning of the columns are apparent from the context and easy for humans to understand, but in emerging applications, labels are needed for autonomous assignment and schema mapping where machine tries to understand the tables. Autonomous label assignment is critical in volume data processing where ad hoc mediation, extraction and querying is involved. We propose an algorithm Lads for Labeling Anonymous Datasets, which can holistically label/annotate tabularWeb document. The algorithm has been tested on anonymous datasets from a number of sites, yielding very promising results.
The Weapon-Target Assignment (WTA) problem is one of the most important problems of military applications of operations research. In this paper, hybrid Nested Partitions (NP) method is proposed to solve WTA problems. The proposed algorithm is named as “Hybrid NP method with intelligent greedy search”. This proposed algorithm combines the advantages of the NP method and intelligent greedy search and shows great efficiency for solving the WTA problem.
The problem of distributing goods from depots to consumers plays an important role in the management of many distribution systems, and therefore when it is programmed efficiently it may yield significant savings. In a typical distribution system, trucks provide pick-up and delivery services to customers that are geographically dispersed in a given area. In many of its applications, the main objective of distribution is to find a set of routes for such trucks, satisfying a variety of constraints, so as to minimize the total distribution cost. This work focuses on a decision model for a real world problem. The problem reveals itself as assignment of trucks to routes by Latex Foam Rubber Products Limited-Kumasi, Ghana. This study addresses the problem of finding an efficient assignment of the limited number of trucks at the company’s disposal to the routes they ply while serving its customers outside the metropolis. In this work we use a solution procedure based on Munkres Assignment Algorithm for optimal assignment of non-homogenous fleet of trucks to a given set of routes, where Latex Foam Rubber Products Limited-Kumasi, distributes its products to its customers.
This book proposed a modified artificial bee colony algorithm for job scheduling problem. Results of the proposed algorithm shows that the efficiency of proposed algorithm is better then the original Genetic Algorithm. This algorithm produced better results for those problems that do not generate exact solution.Finally, Overall motive of these type of algorithms are to get more optimized results from the previous one. Silent Features are: More efficient algorithm, Less time complexity, Easily to understand the proposed concept, Simple language, Helpful for generating new ideas.
Revision with unchanged content. A powerful new search algorithm, the difference map, has had notable success in many fields that involve finding a characteristic point in a high dimensional search space. The algorithm was first applied to a toy model of proteins in 2004. In that context, the difference map was able to find low energy "folds" better than any contemporary search algorithm. In this work, the same algorithm is applied to a realistic protein model. Though the algorithm finds low energy folds for the protein molecule, the energy function used is found to be inadequate to make the lowest energy state of the protein molecule the native conformation. In this work, the algorithm is described, and some example applications are given. Finally, the complete programming implementation of the algorithm to the problem of protein energy minimization is supplied.
The research presents a adaptive TDMA slot assignment algorithm, called MDRAND, which is a modified version of DRAND in clustered wireless sensor networks where cluster nodes need its own time to transmit and receive. Priorities will be given to traffic which is real time applications and best effort applications. It utilizes on the requests which will be send by nodes which would be willing to send. Reservation of slots will be done on upon requests. Simulation results show that time complexity and space complexity is (On) which is similar to that of DRAND algorithm.
The book, which are generalizations of the “Traveling Salesman problems” and “Assignment Problems”. For each of the problems an algorithm based on 'Lexisearch Search Approach? is developed to get an optimal feasible solution using 'Pattern Recognition Approach'.Each problem is studied and the necessary computational results are presented in it, and the actual computer program of the algorithm written in 'C' language is given. In the usual 'Lexi-search Algorithm for the problems developed so far, when a partial word is considered, it is first checked for feasibility. In this context the feasibility checking is easy and hence it is checked first and when it is feasible, the difficult part of calculating the bounds is taken up later, for developing an efficient algorithm. For some problems the difficulty may be the other way, that is, effective bounds can be calculated easily and feasibility checking technique called the 'Pattern Recognition Technique for is used in this book.
The Quadratic Assignment Problem (QAP) described as the problem of assigning a set of facilities to a set of locations. In multi-objective case of QAP more than one flow of items between facilities are considered. The goal is to place the facilities on locations such that the sum of the products between flows and distances is minimal. Multi-objective Ant-Q Algorithm (MOAQ) is an Ant-Q Algorithm that can solve multi-objective optimization problems. MOAQ considers family of agents for each objective function. External-Memory-Based MOAQ Algorithms are introduced to improve performance of MOAQ Algorithm. External Memories are used to keep variable size solution segments or partial permutation sequences from elite solutions that are constructed at the beginning of algorithm. After this initialization phase, a particular ant retrieves a segment or partial permutation sequence from external-memory and constructs solution according to selected segment or partial permutation sequence. External-Memory-Based MOAQ Algorithms are tested using mQAP instances.
In this book we present a new approach to cancelling echoes, mostly occurrence in today’s telecommunication system due to acoustic coupling between loudspeaker and a microphone. The proposed algorithm is a Modified VSS-NLMS-UM algorithm the popularity of this algorithm due to the solve a conflicting requirement of fast convergence and low misadjustment. Most of the algorithm was design under-modeling scenario the proposed algorithm doesn’t require any prior information about acoustic environment. Due to the specific characteristic of this algorithm are equipped with good robustness feature against the near end signal variation and has a low computational complexity and low level data storage. So it’s a reliable candidate for real world application... deman kosale.....
In this book a Fuzzy-based Algorithm for the prediction of next CPU burst has been proposed. This algorithm uses the intelligent fuzzy system to estimate the execution time of a process based on its past behavior. The heart of this system is a database which contains if-then rules. Further, the comparative analysis of the Exponential Average Algorithm and the Fuzzy -based algorithm reflect that the Fuzzy-based approach is more optimal, thus it predicts more closer values to the real CPU-burst than the Exponential Average Algorithm.
The efficient management of Change Requests (CRs) is fundamental for successful software maintenance; however the assignment of CRs to developers is an expensive aspect in this regard, due to the time and expertise demanded. To overcome this, researchers have proposed automated approaches for CR assignment. Although these proposals present advances to this topic, they do not consider many factors inherent to the assignments, such as: developers’ workload, CRs severity, interpersonal relationships, and developers know-how. Actually, as we demonstrate in this work, CR assignment is a complex activity and automated approaches cannot rely on simplistic solutions. Ideally, it is necessary to consider and reason over contextual information in order to provide an effective automation. Beyond investigating all these aspects through an extensive systematic literature review, this work also proposes a context-aware architecture solution to semi-automated assignment of CRs.
This book is focused on the modification of the Backpropagation Through Time algorithm and its implementation on the Recurrent Neural Networks. Our work is inspired and motivated by the results of the Salvetti and Wilamowski experiment focused on the introduction of stochasticity into Backpropagation algorithm on experiments with the XOR problem. The stochasticity can be embedded into different parts of the BP algorithm. We introduced and implemented different types of BP algorithm modifications, which gradually add more stochasticity to the BP algorithm. The goal of this book is to prove, that this stochastic modification is able to learn efficiently and the results are comparable to classical implementation. This stochasticity also brings a simpler implementation of the algorithm, than the classical one, which is especially useful on the Recurrent Neural Networks.
Scheduling is the central concept used in operating system. It helps in choosing the processes for execution. There are many scheduling algorithms available for a multi-programmed operating system like FCFS, SJF, Priority, Round Robin etc. We mainly focused on Round Robin scheduling algorithm. We proposed a new algorithm as titled “A NOVEL APPROACH FOR SCHEDULING”. It is the combination of Round Robin scheduling algorithm and Dynamic Time Quantum. We get better result in terms of Waiting Time, Turnaround Time and number of Context Switch than the Round Robin scheduling algorithm using static time quantum, Average Mid Max Round Robin scheduling algorithm and Min-Max Round Robin scheduling algorithm.
Machine Learning (ML), a field of artificial intelligence, is concerned with the design and development of new algorithms that allow computers to learn behaviors based on observed data. Genetic Folding as a novel algorithm inspired by RNA and DNA folding, GF algorithm thereby offering a simple and inexpensive way of studying problems without a need of implementing complexity models. “Genetic Folding: New Computational Intelligence Models” book is introduced with wide range of experiments the ability of GF as a new AI algorithm. In addition to GF algorithm, the book introduces a novel method named as Alignment Algorithm.
The Evil Within: The Assignment – это первое из двух дополнение к The Evil Within, главной героиней которого является Джулия Кидман, напарница детектива Себастьяна Кастелланоса. В этом дополнении игроки получат часть ответов на те вопросы, которые у них остались после финала оригинальной игры.
In cellular mobile communication, since there will be conversation in progress in case of handover calls, GOS for them should be less than that for newly originating calls. Basically, there are three channel assignment schemes, fixed, dynamic & hybrid channel assignment schemes for handling newly originating and handover calls. In this study, capacity design for cellular mobile network in area inside ring road in Kathmandu valley has been done according to the findings of data collection and analysis of mobile communication service being provided by Nepal Telecom. Capacity planning has been done with GOS equal to 2% for newly originating calls and 1% for handover calls. In addition to capacity planning, efficient channel assignment scheme in terms of blocking and throughput has been evaluated with optimal utilization of the designed capacity for getting minimum blocking for both types of calls maintaining the blocking for handover calls always less than that for newly originating calls. The simulated values of GOS & throughput for all channel assignment schemes at different penetration have been compared and hybrid channel assignment scheme has been found to be most efficient.
Ant colony algorithm and Genetic algorithm are considered as the most important and advanced Evolutionary algorithms. These two algorithms have got extensive real world applications and solutions for optimization problems. One such type is the multiple travelling salesmen problem. The research finds a better solution for this problem and further research on these algorithm would find even more better solutions.
Today, lecturers of introductory courses have to create and evaluate exercises for an enormous number of students. Although they usually receive help from teaching assistants, the effort for evaluating the exercises is huge. This thesis proposes a solution at least for lecturers of algorithm and data structure courses. But the solution may also be useful for courses which cover different topics. The idea is that the lecturer creates an exercise with the help of an algorithm visualisation system only once and all the evaluation is done automatically. Hence, the solution scales perfectly with a growing number of students. In order to create the exercises, the algorithm visualisation system Animal is used. Interactions and especially questions can already be included in animations created by Animal. However, the interaction support of Animal is refactored and mostly rewritten in this thesis, so that it can be extended more easily. In order to automate the whole exercise process, a connection to the learning management system (LMS) Moodle is also developed. In the end, lecturers only have to create an Animal animation and a Moodle assignment—the rest will work automatically: The user starts the animation from within Moodle, answers the questions, closes the animation and the results are stored in Moodle in no time.
Dynamic routing algorithms play an important role in avoiding congestion over road traffic routing. The application of Dynamic Routing algorithm provides the best routes that minimize traffic congestion. The utilization of Ants algorithms in road network routing has been studied extensively by many researchers. One of the popular algorithms which are widely adopted in road traffic routing is the AntNet algorithm. In this work, the utilization of AntNet routing algorithm has been further enhanced and applied for the dynamic routing of road traffic network. This enhanced algorithm, not only, reduces computation which, consequently, decreases the computational complexity; but also, discovers an agile good solution and dynamically conserves the discovered good route. The enhancement of the algorithm is accomplished, by introducing a new type of ants called “check ants” and defining a new data updating strategy for the backward ant.
Data mining is the process of automatically extracting new and useful knowledge hidden in large datasets. This book focuses on the enhancement of following three data mining techniques for achieving the better mining results: • Association Rule Mining (ARM), • Clustering • Classification In Association Rule Mining (ARM), two algorithms known as Apriori algorithm and FP-Growth algorithm have been enhanced for better mining results. An efficient partitional clustering algorithm utilizing the well-known technique, k-means clustering is proposed in this book to tackle the problem of empty clusters. Classification operation usually uses supervised learning methods that induce a classification model from a database. The k-Nearest Neighbor (k-NN) is one of the simplest classification methods used in data mining and machine learning. in this book, the proposed algorithm improved the performance of conventional k-NN algorithm by identifying the optimal value of k.
Update Vehicle Traffic Routing Using Ant Colony Optimization Algorithm is to implement the solution of combinatorial problem, based on the heuristic behavior of ant. This paper focuses on a highly developed solution procedure using ACO algorithm. This helps to solve routing problems easily. It also reflects the method considering flow, distance, cost, and emergency etc. Here, a new algorithm named UVTR (Update Vehicle Traffic Routing) is represented to overcome the complexity of the previous algorithm. It yields the typical process for removing traffic problems in case of flow, distance, cost etc. This formulation is represented with systematic rules based case study for the Dhaka City.
Cross-correlation and related techniques have dominated the Image processing field since the early fifties. Conventional template matching algorithm based on cross-correlation requires complex calculation and large time for object detection, which makes difficult to use them in real time applications. The shortcomings of this class of image matching methods have caused a slow-down in the development of operational automated correlation systems. In the proposed book particle swarm optimization & its variants based algorithm is used for detection of object in image. Implementation of this algorithm reduces the time required for object detection than conventional template matching algorithm. Algorithm can detect object in less number of iteration & hence less time & energy than the complexity of conventional template matching. This feature makes the method capable for real time implementation.
In this thesis, we present the O(n log^2 n) superfast linear least squares Schur algorithm(ssschur). The algorithm we describe illustrates a fast way of solving linear equations or linear least squares problems with low displacement rank. This algorithm is based on the O(n^2) Schur algorithm, sped up via FFT. The algorithm solves an ill-conditioned Toeplitz-like system using Tikhonov regularization. The regularized system solved is Toeplitz-like and is of displacement rank, 4. In this thesis, we also show the effect of the choice of the regularization parameter on the quality of the images reconstructed.
Recent advances in the optimization of fixed time traffic signals have demonstrated a move towards the use of genetic algorithm optimization with traffic network performance evaluated via stochastic microscopic simulation models. This book examines methods for improved optimization. Several modified versions of the genetic algorithm and alternative genetic operators were evaluated on test networks. Application of the CHC search algorithm with real crossover and mutation operators was found to offer improved optimization efficiency over the standard genetic algorithm with binary genetic operators. Computing resources are best utilized by using a single replication of the traffic simulation model with common random numbers for fitness evaluations. Combining the improvements, delay reductions between 13%-32% were obtained over the standard approaches. A coding scheme allowing for complete optimization of signal phasing is proposed. Alternative delay measurements, amendments to genetic operators and modifications to the CHC algorithm are also suggested.
Modern day public transport assignment faces lots of problems. Solutions may exist but needs some form of backings. A sensitivity analysis in most cases could just be sufficient to provide this backing. The developments in this book are very important for students, academic researchers and policy makers. The book provides an extensive review on transit assignment, sensitivity analysis and OmniTRANS. It highlights important conclusions such as:- - Passenger access-egress distances significantly affects their access stop choices. - The travel distance parameter of the generalized cost function has the highest influence on transit assignment - The number of transfers to be made by passengers significantly determines their generalized cost of travel - A carefully conducted partial sensitivity analysis can provide better predictions than a Monte-Carlo sensitivity analysis. As a policy advice to public transport operators in Flanders-Belgium, the developments in this book suggest that; to improve transit assignment, it is sufficient to reduce passengers access-egress distances & the number of transfers either through the institution of more direct transit lines or missing links.