Showing papers on "Local search (optimization) published in 1988"
IBM1
TL;DR: The AP-based lower bound on the optimal solution to ATSP is tightened by using some of the procedures given in [1]; a local search for improving the initial FSO is performed; it uses 3- and 4-changed based procedures.
126 citations
24 Aug 1988
TL;DR: In this paper, a parallel, global search technique emulating natural genetic operators is used to estimate the poles and zeros of a dynamical system and an adaptive controller is designed on the basis of the estimates.
Abstract: Current online identification techniques are recursive and local search techniques. In the present work, it is shown how genetic algorithms, a parallel, global search technique emulating natural genetic operators, can be used to estimate the poles and zeros of a dynamical system. An adaptive controller is designed on the basis of the estimates. Simulations and an experiment show the technique to be satisfactory and to provide unbiased estimates in the presence of colored noise. >
30 citations
Book•
01 Jan 1988
21 citations
TL;DR: In this article, a local search method for scheduling a flexible flow line is presented, which takes into account various line phenomena, such as setups, finite buffers, blocking and starvation, machine breakdowns and downtimes, and the current and subsequent states of the line.
15 citations
TL;DR: A search tree grown from an n -long random file of numerical records is studied and the depth and the total number of comparisons of the search are shown to be asymptotically Gaussian.
12 citations
TL;DR: It is shown that this class of 'catastrophic' examples of TSP can be solved by linear programming relaxation appended with k subtour elimination constraints, and it follows that thisclass of problem instances of T SP can be optimized in polynomial time.
9 citations
11 Apr 1988
TL;DR: The authors show that it is possible to extend the threshold effect of singular-value-decomposition (SVD)-based signal-processing algorithms by using the Prony-Lanczos (P-L) method to lower values of signal-to-noise ratio.
Abstract: Threshold computation is essential in comparing the statistical performance of algorithms when estimating signal parameters. The authors show that it is possible to extend the threshold effect of singular-value-decomposition (SVD)-based signal-processing algorithms by using the Prony-Lanczos (P-L) method to lower values of signal-to-noise ratio. The procedure is comprised of two steps. In the first step, a nonparametric spectrum analysis or beamforming is used to yield a good starting point. This is followed in the second step by the (P-L) algorithm, which performs a local search, a procedure relatively insensitive to outliers. Simulation results, based on the angles between the estimated and true subspaces using the SVD-based algorithm and the P-L method, provide valuable insight. >
7 citations
IBM1
TL;DR: A general optimization algorithm, which in some areas successfully competes with simulated annealing and the Kernighan-Lin algorithm, as well as special heuristics, is presented.
Abstract: A general optimization algorithm, which in some areas successfully competes with simulated annealing and the Kernighan-Lin algorithm, as well as special heuristics, is presented. It gains speed by taking advantage of the structure of the objective function in order to reduce the search space. Results obtained from the implementation of the algorithm on three problems are presented. >
2 citations
15 Jun 1988
TL;DR: A vehicle location searching algorithm is described which uses phase ranging data from several base stations to calculate the position of the transmitter and gives fast, stable, and accurate results in a multipath transmission environment.
Abstract: A vehicle location searching algorithm is described which uses phase ranging data from several base stations to calculate the position of the transmitter. The search algorithm is based on geographical partition and local optimization. Computer simulation shows that the algorithm gives fast, stable, and accurate results in a multipath transmission environment. >
TL;DR: It is shown that, to within closure, local search applied to bases generates all optimal vertices (but not necessarily all optimal bases) for any constraint set if and only if the cone of permitted objective functions is convex.
Abstract: Optimal vertices of multiparametric linear-programming problems can be found by a local search procedure which involves testing only neighbouring vertices for optimality. When degeneracy is present, vertices and bases will not uniquely correspond, but a similar basis exploration procedure can be used. It is shown that, to within closure, local search applied to bases generates all optimal vertices (but not necessarily all optimal bases) for any constraint set if and only if the cone of permitted objective functions is convex. This implies that the procedure is successful if and only if the problem is essentially one of vector optimization.