Convergence Behavior Of Algorithm Research Articles

Optimized metamaterial-loaded fractal antenna using modified hybrid BF-PSO algorithm

The paper proposes optimization of square split-ring resonator (SRR) metamaterial unit cell using modified hybrid bacterial foraging–particle swarm optimization (BF-PSO). Optimized metamaterial unit cells are loaded into novel designed square fractal antenna for its bandwidth enhancement. The presented research is alienated in three phases: Novel design of microstrip line-fed square fractal antenna with defected ground structure is proposed in the initial phase that provides dual band performance. In second phase, with the aim of bandwidth enhancement, quasi-static model of SRR unit cell is used to optimize its structural parameters so that optimized structure resonates at desired frequency region. Modifications are included in hybrid BF-PSO algorithm as per size constraints of SRR unit cell to be optimized and for improving the convergence behavior of algorithm. The performance of modified hybrid BF-PSO algorithm is assessed against four other evolutionary techniques named as classical BFO, chaos PSO, IWO and ABC. In later phase, optimized SRR unit cells are loaded into initially designed square fractal antenna that results in broadband performance suitable for upper S-band and lower C-band wireless applications. The designed square fractal antenna without and with SRR unit cells is fabricated and tested to verify the experimental results.

Enhanced flower pollination algorithm on data clustering

Nature-inspired algorithms are emerging as most compatible algorithms in obtaining near-optimal solution to complex problems. The ability of meta-heuristic algorithm to obtain global optimization solution largely depends on convergence behavior. In order to enhance convergence capability of latest nature-inspired algorithm i.e. flower pollination algorithm (FPA), a modified version is presented. The performance of modified FPA is tested over clustering application. Algorithm is assessed in contrast to bat algorithm, firefly algorithm, and conventional FPAs on 10 clustering data-sets. Out of 10 data-sets, 8 are derived from pattern recognition and 2 are artificially generated. Clustering results are computed in terms of objective function value and CPU time taken at each run. Run length distribution graphs illustrate the convergence behavior of algorithms. Results indicate that the proposed modified FPA outperforms its counterparts both in terms of attaining best fitness value and reducing the CPU time.

On the accurate identification of active set for constrained minimax problems

In this paper, the problem of identifying the active constraints for constrained nonlinear programming and minimax problems at an isolated local solution is discussed. The correct identification of active constraints can improve the local convergence behavior of algorithms and considerably simplify algorithms for inequality constrained problems, so it is a useful adjunct to nonlinear optimization algorithms. Facchinei et al. [F. Facchinei, A. Fischer, C. Kanzow, On the accurate identification of active constraints, SIAM J. Optim. 9 (1998) 14–32] introduced an effective technique which can identify the active set in a neighborhood of a solution for nonlinear programming. In this paper, we first improve this conclusion to be more suitable for infeasible algorithms such as the strongly sub-feasible direction method and the penalty function method. Then, we present the identification technique of active constraints for constrained minimax problems without strict complementarity and linear independence. Some numerical results illustrating the identification technique are reported.

Special Issue on Accurate Solution of Eigenvalue Problems

The occasion for this special issue is the Sixth International Workshop on Accurate Solution of Eigenvalue Problems, which took place at The Pennsylvania State University from May 22–25, 2006. This special issue provides an outlet for papers from the workshop and recognizes advances in the numerical solution of eigenvalue and related problems. This is the second such special issue published in the SIAM Journal on Matrix Analysis and Applications; the first was published in number 4 of volume 28 in connection with the Fifth International Workshop on Accurate Solution of Eigenvalue Problems, which took place in Hagen, Germany, from June 29 to July 1, 2004. The twelve papers in the current issue are concerned with a variety of aspects that arise in the computation of eigenvalues and invariant subspaces: perturbation bounds and sensitivity, accuracy and convergence behavior of algorithms, exploitation of structure in matrices, and particular engineering applications. Thanks go to SIMAX Editor-in-Chief, Henk van der Vorst; guest editors Jesse Barlow, Froilán Dopico, and Zlatko Drmač, who put great effort into the careful and timely review of papers; and Mitch Chernoff, Cherie Trebisky, and other members of the SIAM staff who worked hard to publish this special issue.

Constraints on the convergence behavior of algorithms via reachable set analysis

AbstractMany iterative algorithms can be interpreted as discrete‐time control systems evolving on manifolds. Here, shift parameters act as control inputs. The Adherence structure of the reachable sets provides fundamental limitations on the possible convergence behavior of the algorithm.We analyse the reachable sets of inverse iteration schemes and rational iteration schemes. In particular we show a necessary conditions for the existence of local convergent shift strategies for inverse iteration. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Special Issue on Accurate Solution Of Eigenvalue Problems

The occasion for this special issue is the Sixth International Workshop on Accurate Solution of Eigenvalue Problems, which took place at The Pennsylvania State University from May 22-25, 2006. This special issue provides an outlet for papers from the workshop and recognizes advances in the numerical solution of eigenvalue and related problems. This is the second such special issue published in the SIAM Journal on Matrix Analysis and Applications; the first was published in number 4 of volume 28 in connection with the Fifth International Workshop on Accurate Solution of Eigenvalue Problems, which took place in Hagen, Germany, from June 29 to July 1, 2004. The twelve papers in the current issue are concerned with a variety of aspects that arise in the computation of eigenvalues and invariant subspaces: perturbation bounds and sensitivity, accuracy and convergence behavior of algorithms, exploitation of structure in matrices, and particular engineering applications. Thanks go to SIMAX Editor-in-Chief, Henk van der Vorst; guest editors Jesse Barlow, Froilan Dopico, and Zlatko Drmac, who put great effort into the careful and timely review of papers; and Mitch Chernoff, Cherie Trebisky, and other members of the SIAM staff who worked hard to publish this special issue.

Mean-Convergence behavior of Adaptive Identification Algorithms for Pole-Zero Systems

Adaptive methods for the estimation of unknown system parameters has the advantage of tracking time-varying systems. Identification algorithms for recursive systems produce nonquadratic performance functions. In such problems it is very difficult to estimate the nature of convergence in a stochastic frame work. Recently, it has been shown that the ensemble mean parameter updating equations of IIR adaptive algorithm can be represened by associated ordinary differential equations (ODEs). A method of solving the ODEs in order to analyze the mean- convergence behavior of these algorithms, given the mean description of the input in the form of power spectral density, has been presented recently. In this paper, this procedure is applied to study the convergence behavior of recursive adaptive algorithms applied for the identification of pole-zero systems. Effectiveness of this method is shown through analytical and simulation results.