应用多重交换法设计具有k个陷波频点的等波纹FIR陷波滤波器

本文提出了一种设计等波纹线性相位 FIR陷波滤波器的优化算法。首先介绍了线性相位 FIR滤波器的四种情况 ,然后在此基础上设计了一种应用多重交换算法来处理心电信号的陷波滤波器。此算法相对于均方误差最小准则和最大误差最小化准则具有占用存储空间小和计算时间短的优点。     

Parameters identification of chaotic system by chaotic gravitational search algorithm

In this paper, for the parameter identification problem of chaotic system, a chaotic gravitational search algorithm (CGSA) is proposed. At first, an iterative chaotic map with infinite collapses is introduced and chaotic local search (CLS) is designed, then CLS and basic gravitational search are combined in the procedure frame. The CGSA is composed of coarse gravitational search and fine chaotic local search, while chaotic search seeks the optimal solution further, based on the current best solution found by the coarse gravitational search. In order to show the effectiveness of CGSA, both offline and online parameter identifications of Lorenz system are conducted in comparative experiments, while the performances of CGSA are compared with GA, PSO and GSA. The results demonstrate the effectiveness and efficiency of CGSA in solving the problem of parameter identification of chaotic system, and the improvement to GSA has been verified.     

Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting

Hybridization chaotic mapping functions with optimization algorithms into a support vector regression model has been shown its efficient potential to avoid converging prematurely. It is deserved to explore more possibility by hybridizing with other optimization algorithms. Electricity demand sometimes demonstrates a seasonal tendency due to complicate economic activities or climate cyclic nature. This investigation presents a SVR-based electricity forecasting model which applied a novel hybrid algorithm, namely chaotic gravitational search algorithm (CGSA), to improve the forecasting performance. The proposed CGSA employs the chaotic local search by logistic chaotic mapping function in the iteration of the original GSA to search and refine the current best solution. In addition, seasonal mechanism is also applied to deal with seasonal electricity tendency. A numerical example from an existed reference is used to illustrate the forecasting performance of the proposed SSVRCGSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models.     

Generalized low pass filters and MRA frame wavelets

A tight frame wavelet ψ is an L ²(ℝ) function such that {ψ jk(x)} = {2^j/2 ψ(2 ^j x −k), j, k ∈ ℤ},is a tight frame for L ² (ℝ).We introduce a class of “generalized low pass filters” that allows us to define (and construct) the subclass of MRA tight frame wavelets. This leads us to an associated class of “generalized scaling functions” that are not necessarily obtained from a multiresolution analysis. We study several properties of these classes of “generalized” wavelets, scaling functions and filters (such as their multipliers and their connectivity). We also compare our approach with those recently obtained by other authors.     

Continuous optimization via simulation using Golden Region search

Simulation Optimization (SO) is a class of mathematical optimization techniques in which the objective function can only be numerically evaluated through simulation. In this paper, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. Monte Carlo experiments show that the GR method is efficient compared to three well-established approaches in the literature. We also prove the asymptotic convergence in probability to a global optimum for a large class of random search methods in general and GR in particular.     

A quadratic programming algorithm using conjugate search directions

A quadratic programming algorithm is presented, resembling Beale's 1955 quadratic programming algorithm and Wolfe's Reduced Gradient method. It uses conjugate search directions. The algorithm is conceived as being particularly appropriate for problems with a large Hessian matrix. An experimental computer program has been written to validate the concepts, and has performed adequately, although it has not been used on very large problems. An outline of the solution to the quadratic capacity-constrained transportation problem using the above method is also presented.While engaged in this research the author had a part-time post with the Manpower Services Commission.     

A global optimization algorithm using adaptive random search

A new random-search global optimization is described in which the variance of the step-size distribution is periodically optimized. By searching over a variance range of 8 to 10 decades, the algorithm finds the step-size distribution that yields the best local improvement in the criterion function. The variance search is then followed by a specified number of iterations of local random search where the step-size variance remains fixed. Periodic wide-range searches are introduced to ensure that the process does not stop at a local minimum. The sensitivity of the complete algorithm to various search parameters is investigated experimentally for a specific test problem. The ability of the method to locate global minima is illustrated by an example. The method also displays considerable problem independence, as demonstrated by two large and realistic example problems: (1) the identification of 25 parameters in a nonlinear model of a five-degrees-of-freedom mechanical dynamic system and (2) solution of a 24-parameter inverse problem required to identify a pulse train whose frequency spectrum matched a desired reference spectrum.     

Selecting survivors in genetic algorithm using tabu search strategies

Genetic algorithm (GA) is well-known for its effectiveness in global search and optimization. To balance selection pressure and population diversity is an important issue of designing GA. This paper proposes a novel hybridization of GA and tabu search (TS) to address this issue. The proposed method embeds the key elements of TS—tabu restriction and aspiration criterion—into the survival selection operator of GA. More specifically, the tabu restriction is used to prevent inbreeding for diversity maintenance, and the aspiration criterion is activated to provide moderate selection pressure under the tabu restriction. The interaction of tabu restriction and aspiration criterion enables survivor selection to balance selection pressure and population diversity. The experimental results on numerical and combinatorial optimization problems show that this hybridization can significantly improve GAs in terms of solution quality as well as convergence speed. An empirical analysis further identifies the influences of the TS strategies on the performance of this hybrid GA.     

Using filters to describe congruences and band congruences of semigroups

It is well known that the smallest semilattice congruence can be described via filters. We generalise this result to the smallest left (right) normal band congruences and also to arbitrary semilattice (left normal band, right normal band) congruences, describing them all via filters. To achieve this, we introduce filters relative to arbitrary quasiorders on a semigroup (traditional filters are filters relative to the smallest negative operation-compatible quasiorder). We study congruences which can be described via filters. We show that the lattice of semilattice (left normal band, right normal band) congruences is a homomorphic image of the lattice of negative (right negative, left negative) operation-compatible quasiorders.     

Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm

A variable neighborhood search (VNS) algorithm has been developed to solve the multiple objective redundancy allocation problems (MORAP). The single objective RAP is to select the proper combination and redundancy levels of components to meet system level constraints, and to optimize the specified objective function. In practice, the need to consider two or more conflicting objectives simultaneously increases nowadays in order to assure managers or designers’ demand. Amongst all system level objectives, maximizing system reliability is the most studied and important one, while system weight or system cost minimization are two other popular objectives to consider. According to the authors’ experience, VNS has successfully solved the single objective RAP (Liang and Chen, Reliab. Eng. Syst. Saf. 92:323–331, 2007; Liang et al., IMA J. Manag. Math. 18:135–155, 2007). Therefore, this study aims at extending the single objective VNS algorithm to a multiple objective version for solving multiple objective redundancy allocation problems. A new selection strategy of base solutions that balances the intensity and diversity of the approximated Pareto front is introduced. The performance of the proposed multi-objective VNS algorithm (MOVNS) is verified by testing on three sets of complex instances with 5, 14 and 14 subsystems respectively. While comparing to the leading heuristics in the literature, the results show that MOVNS is able to generate more non-dominated solutions in a very efficient manner, and performs competitively in all performance measure categories. In other words, computational results reveal the advantages and benefits of VNS on solving multi-objective RAP.     

Solution of goal programming models using a basic taboo search algorithm

Goal programming is a very powerful technique for solving multiple objective optimisation problems. It has been successfully applied to numerous diverse real life problems. In this paper a Taboo search based method is developed to solve preemptive goal programming problems. The method can easily be applied to any kind of preemptive goal programming problems.     

Design and simulation of sensor fusion using symbolic engines

Sensor fusion is the art of estimating accurate information from noisy multi-sensor data. Due to the complexity of stochastic sensor errors, design and testing of sensor fusion algorithms have been always challenging. Existing design approaches are mainly mission specific with fixed system models that do not verify if the filter can estimate hidden errors. To address these challenges, this paper presents a flexible design and simulation environment for sensor fusion. The environment utilizes symbolic engine as a flexible representation of system models to enable flexible and accurate generation of linearized error models. Inverse kinematic is used to generate pseudo-error-free inertial data to test the ability of the filte to estimate sensor errors. The developed environment is demonstrated on an Attitude and Heading Reference System using Extended Kalman Filter. The demonstration includes both simulation and experimental tests. The designed filter supports both loosely and tightly coupled filtering approaches.     

Computational experience using an edge search algorithm for linear reverse convex programs

This paper presents computational experience with a rather straight forward implementation of an edge search algorithm for obtaining the globally optimal solution for linear programs with an additional reverse convex constraint. The paper's purpose is to provide a collection of problems, with known optimal solutions, and performance information for an edge search implementation so that researchers may have some benchmarks with which to compare new methods for reverse convex programs or concave minimization problems. There appears to be nothing in the literature that provides computational experience with a basic edge search procedure. The edge search implementation uses a depth first strategy. As such, this paper's implementation of the edge search algorithm is a modification of Hillestad's algorithm [11]. A variety of test problems is generated by using a modification of the method of Sung and Rosen [20], as well as a new method that is presented in this paper. Test problems presented may be obtained at ftp://newton.ee.ucla.edu/nonconvex/pub/.     

Enhancement of a viscous-inviscid-interaction airfoil analysis code using the parallel direct search algorithm

The coupling mechanism for an existing viscous-inviscid-interaction (VII) code, developed for the analysis of two-dimensional, turbulent, attached flow around airfoils, is enhanced using the parallel direct search (PDS) optimization algorithm. It is demonstrated that this parallel processing implemented optimization scheme leads to faster convergence of the VII code, and therefore, requires less computational time when the number of optimization (or design) variables is low, and a moderate number of processors are available. As the number of design variables increases, more processors are required to maintain this advantage. Results are presented for the NACA-0012 and the RAE-2822 airfoils. The quality of the results obtained is satisfactory and confirms that the enhanced VII code can be an acceptable alternative to reduced Navier-Stokes solvers as an airfoil analysis tool.     

A heterogeneous cooperative parallel search of branch-and-bound method and tabu search algorithm

In this study, we present a heterogeneous cooperative parallel search that integrates branch-and-bound method and tabu search algorithm. These two algorithms perform searches in parallel and cooperate by asynchronously exchanging information about the best solutions found and new initial solutions for tabu search. The rapid production of a good solution from the tabu search process provides the branch-and-bound process with a better feasible solution to accelerate the elimination of subproblems that do not contain an optimal solution. The new initial solution produced from the subproblem with a least-cost lower bound of the branch-and-bound method suggests the best potential area for tabu search to explore. We use a master-slave model to reduce the complexity of communication and enhance the performance of data exchange. A branch-and-bound process is used as the master process to control the exchange of information and the termination of computation. Several tabu search processes are executed simultaneously as the slave processes and cooperate by asynchronously exchanging information on the best solutions found and the new initial solutions by the master process of branch-and-bound. Based on the computation experiments of solving traveling salesman problems (TSP), the proposed heterogeneous parallel search algorithm outperforms a conventional parallel branch-and-bound method and a conventional parallel tabu search. We also present the computational results showing the efficiency of heterogeneous cooperative parallel search when we use more processors to accelerate search time. Thus, the proposed heterogeneous parallel search algorithm achieves linear accelerations.     

A multi-objective genetic algorithm for robust flight scheduling using simulation

Traditional methods of developing flight schedules generally do not take into consideration disruptions that may arise during actual operations. Potential irregularities in airline operations such as equipment failure are not adequately considered during the planning stage of a flight schedule. As such, flight schedules cannot be met as planned and their performance is compromised, which may eventually lead to huge losses in revenue for airlines. In this paper, we seek to improve the robustness of a flight schedule by re-timing its departure times. The problem is modeled as a multi-objective optimization problem, and a multi-objective genetic algorithm (MOGA) is developed to solve the problem. To evaluate flight schedules, SIMAIR 2.0, a simulation model which simulates airline operations under operational irregularities, has been employed. The simulation results indicate that we are able to develop schedules with better operation costs and on-time performance through the application of MOGA.     

A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic

In this article, a new memetic algorithm has been proposed to solve job shop scheduling problems (JSSPs). The proposed method is a genetic-algorithm-based approach combined with a local search heuristic. The proposed local search heuristic is based on critical operations. It removes the critical operations and reassigns them to a new position to improve the fitness value of the schedule. Moreover, in this article, a new fitness function is introduced for JSSPs. The new fitness function called priority-based fitness function is defined in three priority levels to improve the selection procedure. To show the generality of our proposed method, we apply it to three different types of job scheduling problems including classical, flexible and multi-objective flexible JSSPs. The experiment results show the efficiency of the proposed fitness function. In addition, the results show that incorporating local search not only offers better solutions but also improves the convergence rate. Compared to the state-of-the-art algorithms, the proposed method outperforms the existing methods in classical JSSPs and offers competitive solutions in other types of scheduling problems.     

Design of two-dimensional recursive filters

The paper discusses a numerical algorithm for obtaining rational approximations to a given continuous function. This method offers the promise of a comprehensive solution to the problems of two-dimensional recursive digital filter design. This technique, called Chebyshev design, avoids the stability test inherent in present design methods.