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VLSI cell placement techniques
 ACM Computing Surveys
, 1991
"... VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasi ..."
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Cited by 76 (0 self)
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VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard ce11and macro
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 73 (8 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
An Investigation of Genetic Algorithms for Facility Layout Problems
 University of Edinburgh
, 1995
"... The Facility Layout Problem (FLP) concerns minimising total traffic cost between facilities in a particular location under given conditions including facility size and traffic between each pair of them. Because of its NPcompleteness, many suboptimal methods, which look for reasonably good solutions ..."
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Cited by 5 (0 self)
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The Facility Layout Problem (FLP) concerns minimising total traffic cost between facilities in a particular location under given conditions including facility size and traffic between each pair of them. Because of its NPcompleteness, many suboptimal methods, which look for reasonably good solutions, have been suggested. Although many papers exist which compare the performance of these methods with each other, the work is limited in the following ways: benchmark tests were done only on FLPs consisting of identical facilities; most of the algorithms being compared relied on deterministic approaches. Genetic Algorithms (GAs), which use a stochastic approach, have been used with some success for a number of NPcomplete problems, typically finding good answers but not necessarily the best. However, a range of other approaches, from traditional operations research to simulated annealing, are possible. Moreover, a GA itself can be varied in many ways. So, in this research project, not only t...
A MetaHeuristic with Orthogonal Experiment for the Set Covering Problem
 Journal of Mathematical Modelling and Algorithms
, 2004
"... This paper reports an evolutionary metaheuristic incorporating fuzzy evaluation for some largescale set covering problems originating from the public transport industry. First, five factors characterized by fuzzy membership functions are aggregated to evaluate the structure and generally the goodn ..."
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Cited by 2 (1 self)
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This paper reports an evolutionary metaheuristic incorporating fuzzy evaluation for some largescale set covering problems originating from the public transport industry. First, five factors characterized by fuzzy membership functions are aggregated to evaluate the structure and generally the goodness of a column. This evaluation function is incorporated into a refined greedy algorithm to make column selection in the process of constructing a solution. Secondly, a selfevolving algorithm is designed to guide the constructing heuristic to build an initial solution and then improve it. In each generation an unfit portion of the working solution is removed. Broken solutions are repaired by the constructing heuristic until stopping conditions are reached. Orthogonal experimental design is used to set the system parameters efficiently, by making a small number of trials. Computational results are presented and compared with a mathematical programming method and a GAbased heuristic.
unknown title
"... Placement of standard cells is a part of physical VLSI chip design. In order to achieve high performance, area of the chip and lengths of wires connecting cells have to be minimized. In the placement step, the goal is to place cells in such a way that total wirelength is as short as possible. Since ..."
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Placement of standard cells is a part of physical VLSI chip design. In order to achieve high performance, area of the chip and lengths of wires connecting cells have to be minimized. In the placement step, the goal is to place cells in such a way that total wirelength is as short as possible. Since this problem is NPhard, heuristic techniques have to be applied. Modern approaches include simulated annealing and genetic algorithms. In this paper, we discuss those methods and show that they can be improved by combination. A heuristic technique called parallel recombinative simulated annealing (PRSA) is described. It integrates features of both simulated annealing and genetic algorithms. Behavior of PRSA is studied with respect to different parameter settings. 1 Standard Cell Placement Since integration in VLSI (Very Large Scale Integration) design
ACCELERATING MULTIOBJECTIVE VLSI CELL PLACEMENT WITH PARALLEL EVOLUTIONARY/TABU SEARCH HEURISTICS
"... Multiobjective combinatorial optimization problems in various disciplines remain to be the focus of extensive research due to their inherent hard nature and difficulty of finding nearoptimal solutions. Iterative heuristics like Tabu Search (TS) and Simulated Evolution (SimE) have successfully been ..."
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Multiobjective combinatorial optimization problems in various disciplines remain to be the focus of extensive research due to their inherent hard nature and difficulty of finding nearoptimal solutions. Iterative heuristics like Tabu Search (TS) and Simulated Evolution (SimE) have successfully been employed to solve a range of such optimization problems [1]. These heuristics are able to obtain high quality solutions, but for most reallife large size problems they may have huge runtime requirements. Parallelization of these heuristics is one of the adopted practical approach to achieve the solutions within acceptable runtimes. In this paper we address a hard multiobjective optimization problem namely, VLSI cell placement [2] with three possibly conflicting objectives: interconnect wirelength, power dissipation, and timing performance. Two heuristics namely, parallel tabu search (TS) and parallel simulated evolution (SimE) are presented. Fuzzy rules are used to design a multiobjective aggregate cost function. The parallel TS implementation is a based on a synchronous candidate list partitioning model, whereas the parallel SimE implementation is based on random distribution of rows to processors [3, 4]. For comparison purposes, a parallel genetic algorithm (GA) based on the island model [5], and a parallel simulated annealing (SA) based on the asynchronous multipleMarkov chain [6] are also implemented. Results of experiments on ISCAS85/89 benchmark circuits are presented, with solution quality and speedup used as metrics for the comparative/relative evaluation of the presented heuristics. 1.
A Heuristics Based Approach for Cellular Mobile Network Planning ABSTRACT
"... Designing and planning of the switching, signaling and support network is a fairly complex process in cellular mobile network. In this paper, the problem of assigning cells to switches in cellular mobile network, which is considered a planning problem, is addressed. The cell to switch assignment pro ..."
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Designing and planning of the switching, signaling and support network is a fairly complex process in cellular mobile network. In this paper, the problem of assigning cells to switches in cellular mobile network, which is considered a planning problem, is addressed. The cell to switch assignment problem which falls under the category of the Quadratic Assignment Problem (QAP) is a proven NP– hard problem. Further, the problem is modelled to include an additional constraint in the formulation. The additional constraint is of the maximum number of switch ports that are used for a cell’s Base Station Transceiver System (BTS) connectivity to the switch. The addition of the constraint on the number of ports on a switch has immense practical significance. This paper presents a non– deterministic heuristic based on Simulated Evolution (SimE) iterative algorithm to provide solutions. The methods adopted in this paper are a completely innovative formulation of the problem and involve application of Evolutionary Computing for this complex problem that may be extended to solutions of similar problems in VLSI design, distributed computing and many other applications. 1.
BY
, 1988
"... Two algorithms for channel routing using simulated annealing are presented. Many of the channel routers of the past are for the most part based on greedy algorithms in which special heuristics are applied to generate monotonic improvement. These algorithms are called greedy because they d e r from i ..."
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Two algorithms for channel routing using simulated annealing are presented. Many of the channel routers of the past are for the most part based on greedy algorithms in which special heuristics are applied to generate monotonic improvement. These algorithms are called greedy because they d e r from inappropriate selections, getting stuck at suboptimal solutions. Simulated annealing is an optimization methodology which allows the solution process to back up out of local minima that may be encountered by inappropriate selections. By properly controlling the annealing proccss. it is very likely that the optimal solution to an NPcomplete problem such as channel routing may be found. Previous simulated annealing channel routers only permitted transformations which resulted in a routing without overlapping between nonconnected wires. The algorithm presented here proposes very relaxed restrictions on the types of allowable transformations. including overlapping nets. By freeing that restriction and controlling overlap situations with an appropriate cost function. the algorithm becomes very flexible and can be applied to many extensions of channel routing. The selection of the transformation utilizes a number of heuristics. still retaining the pseudorandom nature of simulated annealing.
Designing Cellular Mobile Networks Using Non–Deterministic Iterative Heuristics Abstract
"... Network planning in the highly competitive, demandadaptive and rapidly growing cellular telecommunications industry is a fairly complex and crucial issue. It comprises collective optimization of the supporting, switching, signaling and interconnection networks to minimize costs while observing impo ..."
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Network planning in the highly competitive, demandadaptive and rapidly growing cellular telecommunications industry is a fairly complex and crucial issue. It comprises collective optimization of the supporting, switching, signaling and interconnection networks to minimize costs while observing imposed infrastructure constraints. This work focuses on the problem of assigning cells to switches, which comprise the Base Station Controller and Mobile Switching Center, in a cellular mobile network. As a classic instance of the NPhard Quadratic Assignment Problem (QAP), deterministic algorithms are incapable of finding optimal solutions in the vast complex search space in polynomial time. Hence, a randomized, heuristic algorithm, such as Simulated Evolution is used in this work to optimize the transmission costs in cellular networks. The results achieved are compared with existing methods available in literature.