Abstract: This paper addresses a stock-cutting problem with rotation of items and without the guillotine cutting constraint. In order to solve the large-scale problem effectively and efficiently, we propose a simple but fast heuristic algorithm. It is shown that this heuristic outperforms the latest published algorithms for large-scale problem instances.
Abstract: In this paper, a design methodology to implement low-power and high-speed 2nd order recursive digital Infinite Impulse Response (IIR) filter has been proposed. Since IIR filters suffer from a large number of constant multiplications, the proposed method replaces the constant multiplications by using addition/subtraction and shift operations. The proposed new 6T adder cell is used as the Carry-Save Adder (CSA) to implement addition/subtraction operations in the design of recursive section IIR filter to reduce the propagation delay. Furthermore, high-level algorithms designed for the optimization of the number of CSA blocks are used to reduce the complexity of the IIR filter. The DSCH3 tool is used to generate the schematic of the proposed 6T CSA based shift-adds architecture design and it is analyzed by using Microwind CAD tool to synthesize low-complexity and high-speed IIR filters. The proposed design outperforms in terms of power, propagation delay, area and throughput when compared with MUX-12T, MCIT-7T based CSA adder filter design. It is observed from the experimental results that the proposed 6T based design method can find better IIR filter designs in terms of power and delay than those obtained by using efficient general multipliers.
Abstract: Proteins or genes that have similar sequences are likely to perform the same function. One of the most widely used techniques for sequence comparison is sequence alignment. Sequence alignment allows mismatches and insertion/deletion, which represents biological mutations. Sequence alignment is usually performed only on two sequences. Multiple sequence alignment, is a natural extension of two-sequence alignment. In multiple sequence alignment, the emphasis is to find optimal alignment for a group of sequences. Several applicable techniques were observed in this research, from traditional method such as dynamic programming to the extend of widely used stochastic optimization method such as Genetic Algorithms (GAs) and Simulated Annealing. A framework with combination of Genetic Algorithm and Simulated Annealing is presented to solve Multiple Sequence Alignment problem. The Genetic Algorithm phase will try to find new region of solution while Simulated Annealing can be considered as an alignment improver for any near optimal solution produced by GAs.
Abstract: This paper studies the optimum design for reducing
optical loss of an 8x8 mechanical type optical switch due to the
temperature change. The 8x8 optical switch is composed of a base, 8
input fibers, 8 output fibers, 3 fixed mirrors and 17 movable mirrors.
First, an innovative switch configuration is proposed with
thermal-compensated design. Most mechanical type optical switches
have a disadvantage that their precision and accuracy are influenced
by the ambient temperature. Therefore, the thermal-compensated
design is to deal with this situation by using materials with different
thermal expansion coefficients (α). Second, a parametric modeling
program is developed to generate solid models for finite element
analysis, and the thermal and structural behaviors of the switch are
analyzed. Finally, an integrated optimum design program, combining
Autodesk Inventor Professional software, finite element analysis
software, and genetic algorithms, is developed for improving the
thermal behaviors that the optical loss of the switch is reduced. By
changing design parameters of the switch in the integrated design
program, the final optimum design that satisfies the design constraints
and specifications can be found.
Abstract: This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects [27] whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest [19]. Both the methods are stochastic in nature and have been successfully applied to solve many Non Polynomial hard problems. Results obtained show that Genetic algorithms out perform Ant Colony optimization technique when tested on the VLSI circuit bi-partitioning problem.
Abstract: Honeycomb sandwich panels are increasingly used in the construction of space vehicles because of their outstanding strength, stiffness and light weight properties. However, the use of honeycomb sandwich plates comes with difficulties in the design process as a result of the large number of design variables involved, including composite material design, shape and geometry. Hence, this work deals with the presentation of an optimal design of hexagonal honeycomb sandwich structures subjected to space environment. The optimization process is performed using a set of algorithms including the gravitational search algorithm (GSA). Numerical results are obtained and presented for a set of algorithms. The results obtained by the GSA algorithm are much better compared to other algorithms used in this study.
Abstract: We present a visualization technique for radial drawing of trees consisting of two slightly different algorithms. Both of them make use of node-link diagrams for visual encoding. This visualization creates clear drawings without edge crossing. One of the algorithms is suitable for real-time visualization of large trees, as it requires minimal recalculation of the layout if leaves are inserted or removed from the tree; while the other algorithm makes better utilization of the drawing space. The algorithms are very similar and follow almost the same procedure but with different parameters. Both algorithms assign angular coordinates for all nodes which are then converted into 2D Cartesian coordinates for visualization. We present both algorithms and discuss how they compare to each other.
Abstract: This paper focuses on the data-driven generation
of fuzzy IF...THEN rules. The resulted fuzzy rule base can be
applied to build a classifier, a model used for prediction, or
it can be applied to form a decision support system. Among
the wide range of possible approaches, the decision tree and
the association rule based algorithms are overviewed, and two
new approaches are presented based on the a priori fuzzy
clustering based partitioning of the continuous input variables.
An application study is also presented, where the developed
methods are tested on the well known Wisconsin Breast Cancer
classification problem.
Abstract: This paper presents the automated methods employed
for extracting craniofacial landmarks in white light images as part of
a registration framework designed to support three neurosurgical
procedures. The intraoperative space is characterised by white light
stereo imaging while the preoperative plan is performed on CT scans.
The registration aims at aligning these two modalities to provide a
calibrated environment to enable image-guided solutions. The
neurosurgical procedures can then be carried out by mapping the
entry and target points from CT space onto the patient-s space. The
registration basis adopted consists of natural landmarks (eye corner
and ear tragus). A 5mm accuracy is deemed sufficient for these three
procedures and the validity of the selected registration basis in
achieving this accuracy has been assessed by simulation studies. The
registration protocol is briefly described, followed by a presentation
of the automated techniques developed for the extraction of the
craniofacial features and results obtained from tests on the AR and
FERET databases. Since the three targeted neurosurgical procedures
are routinely used for head injury management, the effect of
bruised/swollen faces on the automated algorithms is assessed. A
user-interactive method is proposed to deal with such unpredictable
circumstances.
Abstract: Optical network uses a tool for routing called Latin
router. These routers use particular algorithms for routing. For
example, we can refer to LDF algorithm that uses backtracking (one
of CSP methods) for problem solving. In this paper, we proposed
new approached for completion routing table (DRA&CRA
algorithm) and compare with pervious proposed ways and showed
numbers of backtracking, blocking and run time for DRA algorithm
less than LDF and CRA algorithm.
Abstract: Mobile ad hoc network is a collection of mobile
nodes communicating through wireless channels without any
existing network infrastructure or centralized administration.
Because of the limited transmission range of wireless network
interfaces, multiple "hops" may be needed to exchange data
across the network. Consequently, many routing algorithms
have come into existence to satisfy the needs of
communications in such networks. Researchers have
conducted many simulations comparing the performance of
these routing protocols under various conditions and
constraints. One question that arises is whether speed of nodes
affects the relative performance of routing protocols being
studied. This paper addresses the question by simulating two
routing protocols AODV and DSDV. Protocols were
simulated using the ns-2 and were compared in terms of
packet delivery fraction, normalized routing load and average
delay, while varying number of nodes, and speed.
Abstract: With the fast progression of data exchange in electronic way, information security is becoming more important in data storage and transmission. Because of widely using images in industrial process, it is important to protect the confidential image data from unauthorized access. In this paper, we analyzed current image encryption algorithms and compression is added for two of them (Mirror-like image encryption and Visual Cryptography). Implementations of these two algorithms have been realized for experimental purposes. The results of analysis are given in this paper.
Abstract: Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for Thyristor Controlled Series Compensator (TCSC)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a TCSC-based controller, to enhance power system stability.
Abstract: In this paper a combination approach of two heuristic-based algorithms: genetic algorithm and tabu search is proposed. It has been developed to obtain the least cost based on the split-pipe design of looped water distribution network. The proposed combination algorithm has been applied to solve the three well-known water distribution networks taken from the literature. The development of the combination of these two heuristic-based algorithms for optimization is aimed at enhancing their strengths and compensating their weaknesses. Tabu search is rather systematic and deterministic that uses adaptive memory in search process, while genetic algorithm is probabilistic and stochastic optimization technique in which the solution space is explored by generating candidate solutions. Split-pipe design may not be realistic in practice but in optimization purpose, optimal solutions are always achieved with split-pipe design. The solutions obtained in this study have proved that the least cost solutions obtained from the split-pipe design are always better than those obtained from the single pipe design. The results obtained from the combination approach show its ability and effectiveness to solve combinatorial optimization problems. The solutions obtained are very satisfactory and high quality in which the solutions of two networks are found to be the lowest-cost solutions yet presented in the literature. The concept of combination approach proposed in this study is expected to contribute some useful benefits in diverse problems.
Abstract: During last decades, developing multi-objective
evolutionary algorithms for optimization problems has found
considerable attention. Flexible job shop scheduling problem, as an
important scheduling optimization problem, has found this attention
too. However, most of the multi-objective algorithms that are
developed for this problem use nonprofessional approaches. In
another words, most of them combine their objectives and then solve
multi-objective problem through single objective approaches. Of
course, except some scarce researches that uses Pareto-based
algorithms. Therefore, in this paper, a new Pareto-based algorithm
called controlled elitism non-dominated sorting genetic algorithm
(CENSGA) is proposed for the multi-objective FJSP (MOFJSP). Our
considered objectives are makespan, critical machine work load, and
total work load of machines. The proposed algorithm is also
compared with one the best Pareto-based algorithms of the literature
on some multi-objective criteria, statistically.
Abstract: The “PYRAMIDS" Block Cipher is a symmetric encryption algorithm of a 64, 128, 256-bit length, that accepts a variable key length of 128, 192, 256 bits. The algorithm is an iterated cipher consisting of repeated applications of a simple round transformation with different operations and different sequence in each round. The algorithm was previously software implemented in Cµ code. In this paper, a hardware implementation of the algorithm, using Field Programmable Gate Arrays (FPGA), is presented. In this work, we discuss the algorithm, the implemented micro-architecture, and the simulation and implementation results. Moreover, we present a detailed comparison with other implemented standard algorithms. In addition, we include the floor plan as well as the circuit diagrams of the various micro-architecture modules.
Abstract: A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP-Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature.
Abstract: The main criteria of designing in the most hydraulic
constructions essentially are based on runoff or discharge of water. Two of those important criteria are runoff and return period. Mostly,
these measures are calculated or estimated by stochastic data.
Another feature in hydrological data is their impreciseness.
Therefore, in order to deal with uncertainty and impreciseness, based
on Buckley-s estimation method, a new fuzzy method of evaluating hydrological measures are developed. The method introduces
triangular shape fuzzy numbers for different measures in which both
of the uncertainty and impreciseness concepts are considered. Besides, since another important consideration in most of the
hydrological studies is comparison of a measure during different
months or years, a new fuzzy method which is consistent with special form of proposed fuzzy numbers, is also developed. Finally, to
illustrate the methods more explicitly, the two algorithms are tested on one simple example and a real case study.
Abstract: In the self-stabilizing algorithmic paradigm, each node has a local view of the system, in a finite amount of time the system converges to a global state with desired property. In a graph G =
(V, E), a subset S C V is a 2-packing if Vi c V: IN[i] n SI
Abstract: The emergence of the Internet has brewed the
revolution of information storage and retrieval. As most of the
data in the web is unstructured, and contains a mix of text,
video, audio etc, there is a need to mine information to cater to
the specific needs of the users without loss of important
hidden information. Thus developing user friendly and
automated tools for providing relevant information quickly
becomes a major challenge in web mining research. Most of
the existing web mining algorithms have concentrated on
finding frequent patterns while neglecting the less frequent
ones that are likely to contain outlying data such as noise,
irrelevant and redundant data. This paper mainly focuses on
Signed approach and full word matching on the organized
domain dictionary for mining web content outliers. This
Signed approach gives the relevant web documents as well as
outlying web documents. As the dictionary is organized based
on the number of characters in a word, searching and retrieval
of documents takes less time and less space.