188 research outputs found

    Efficient optimisation of structures using tabu search

    Get PDF
    This paper presents a novel approach to the optimisation of structures using a Tabu search (TS) method. TS is a metaheuristic which is used to guide local search methods towards a globally optimal solution by using flexible memory cycles of differing time spans. Results are presented for the well established ten bar truss problem and compared to results published in the literature. In the first example a truss is optimised to minimise mass and the results compared to results obtained using an alternative TS implementation. In the second example, the problem has multiple objectives that are compounded into a single objective function value using game theory. In general the results demonstrate that the TS method is capable of solving structural optimisation problems at least as efficiently as other numerical optimisation approaches

    Security provision for biometric authentication systems using Enhanced Nelder Mead Algorithm

    Get PDF
    Since, their are numerous advantages of biometrics-based authentication systems over traditional security systems based on knowledge, they are susceptible to attacks that can decrease their security significantly. We analyze these attacks in the multibiometric system. We propose an attack system that uses a hill climbing procedure to synthesize the targeted templates and evaluate its achievability with experimental results conducted on large databases. Hill climbing attack is nothing but security attack based on generating artificial data, after analyzing the output; updating such data, so as to improve the output. This is done repeatedly till output is desire output. So that, several actions can be utilized to decrease the probability of such attacks and their result are also presented. Some of the measures are uniform quantization techniques, non-uniform quantization techniques and many more. We are using uniform quantization, as quantization is the process of mapping a set of continuous pixel values into a finite numbers of possible values. The template division can be done on the basis of uniform quantized method which replicates the principle of uniform or linear quantizer has all the quantization levels uniformly distributed in the interval

    Static and dynamic inventory models under inflation, time value of money and permissible delay in payment

    Get PDF
    In this research a number of mathematical models were developed for static and dynamic deterministic single-item inventory systems. Economic factors such as inflation, time value of money and permissible delay in payment were considered in developing the models. Nonlinear optimization techniques were used to obtain the optimal policies for the systems.;First, a static single-item inventory model was considered in which shortages are allowed and a delay is permitted in payment. In this case, suppliers allow the customers to settle their accounts after a fixed delay period during which no interest is charged.;An extension of the model was then considered in which all cost components of the model are subject to inflation and discounting, with constant rates over the planning horizon. The mathematical model of the system was developed and a nonlinear optimization technique, Hooke and Jeeves search method, was used to obtain the optimal policies for the system.;A dynamic deterministic single-item inventory model was also considered in which the demand was assumed to be a linear function of time. Suppliers allow for a delay in payment and the cost components are subject to inflation and discounting with constant rates and continuous compounding. The Golden search technique was used to obtain the optimum length of replenishment cycle such that the total cost is minimized.;Computer applications using Visual Basic and Mathematics were developed and several numerical example were solved

    An improved exploratory search technique for pure integer linear programming problems

    Get PDF
    The development is documented of a heuristic method for the solution of pure integer linear programming problems. The procedure draws its methodology from the ideas of Hooke and Jeeves type 1 and 2 exploratory searches, greedy procedures, and neighborhood searches. It uses an efficient rounding method to obtain its first feasible integer point from the optimal continuous solution obtained via the simplex method. Since this method is based entirely on simple addition or subtraction of one to each variable of a point in n-space and the subsequent comparison of candidate solutions to a given set of constraints, it facilitates significant complexity improvements over existing techniques. It also obtains the same optimal solution found by the branch-and-bound technique in 44 of 45 small to moderate size test problems. Two example problems are worked in detail to show the inner workings of the method. Furthermore, using an established weighted scheme for comparing computational effort involved in an algorithm, a comparison of this algorithm is made to the more established and rigorous branch-and-bound method. A computer implementation of the procedure, in PC compatible Pascal, is also presented and discussed

    Performance Comparison of the Specialized Alpha Male Genetic Algorithm With Some Evolutionary Algorithms

    Get PDF
    DergiPark: 452095trakyasobedAlpha Male GeneticAlgorithms are sexist and population based optimization tools that mimic theswarm behavior of animals. The algorithm consists on a socially partitionedpopulation of individuals where the partitions are formed by sexual selectionof females. In this paper, we suggest to use Linear Crossover and Hooke-Jeevesmethod for crossover and hybridization operators of Alpha Male GeneticAlgorithms, respectively. We perform a simulation study using a set ofwell-known test functions to reveal performance differences between thespecialized algorithm and some other well-known optimization techniquesincluding Genetic Algorithms, Differential Evolution, Particle SwarmOptimization, and Artificial Bee Colony Optimization. Simulation results showthat the specialized algorithm outperforms its counterparts in most of thecases.Alfa erkek genetikalgoritmalar cinsiyet farkı gözeten ve hayvan gruplarının hareketlerini takliteden topluluk tabanlı bir optimizasyon aracıdır. Algoritma, dişilerin eş seçimiile oluşturduğu sosyal olarak bölünmüş birey topluluklarına dayanmaktadır. Çalışmada,Alfa Erkek Genetik Algoritma’nın çaprazlama ve hibritleşme operatörü olaraksırasıyla Doğrusal Çaprazlama ve Hooke-Jeeves yöntemi kullanılmasıönerilmiştir. Çalışma kapsamında özelleştirilmiş algoritma ile GenetikAlgoritmalar, Diferansiyel Evrim, Parçacık Sürü Optimizasyonu ve Yapay ArıKolonisi Optimizasyonu gibi iyi bilinen algoritmalar arasındaki performansfarklılıklarını ortaya çıkarabilmek için bilinen test fonksiyonları ile bir simülasyonçalışması gerçekleştirilmiştir. Simülasyon sonuçları, özelleştirilmişalgoritmanın çoğu durumda daha iyi performans sergilediğini göstermiştir

    Generalised Pattern Search Based on Covariance Matrix Diagonalisation

    Get PDF
    Pattern Search is a family of gradient-free direct search methods for numerical optimisation problems. The characterising feature of pattern search methods is the use of multiple directions spanning the problem domain to sample new candidate solutions. These directions compose a matrix of potential search moves, that is the pattern. Although some fundamental studies theoretically indicate that various directions can be used, the selection of the search directions remains an unaddressed problem. The present article proposes a procedure for selecting the directions that guarantee high convergence/high performance of pattern search. The proposed procedure consists of a fitness landscape analysis to characterise the geometry of the problem by sampling points and selecting those that whose objective function values are below a threshold. The eigenvectors of the covariance matrix of this distribution are then used as search directions for the pattern search. Numerical results show that the proposed method systematically out-performs its standard counterpart and is competitive with modern complex direct search and metaheuristic methods

    NLINLS: a Differential Evolution based nonlinear least squares Fortran 77 program

    Get PDF
    This paper provides the list of Fortran 77 codes of nonlinear least squares using Differential Evolution as the minimizer algorithm. It has been tested on a number of difficult nonlinear least squares problems (taken from NIST, USA including CPC-X Software challenge problems). Help on how to use the program also is provided.Nonlinear least squares; Differential Evolution; Fortran 77

    Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

    Get PDF
    Constrained optimization of high-dimensional numerical problems plays an important role in many scientific and industrial applications. Function evaluations in many industrial applications are severely limited and no analytical information about objective function and constraint functions is available. For such expensive black-box optimization tasks, the constraint optimization algorithm COBRA was proposed, making use of RBF surrogate modeling for both the objective and the constraint functions. COBRA has shown remarkable success in solving reliably complex benchmark problems in less than 500 function evaluations. Unfortunately, COBRA requires careful adjustment of parameters in order to do so. In this work we present a new self-adjusting algorithm SACOBRA, which is based on COBRA and capable to achieve high-quality results with very few function evaluations and no parameter tuning. It is shown with the help of performance profiles on a set of benchmark problems (G-problems, MOPTA08) that SACOBRA consistently outperforms any COBRA algorithm with fixed parameter setting. We analyze the importance of the several new elements in SACOBRA and find that each element of SACOBRA plays a role to boost up the overall optimization performance. We discuss the reasons behind and get in this way a better understanding of high-quality RBF surrogate modeling
    corecore