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On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts  Towards Memetic Algorithms
, 1989
"... Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that ..."
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Cited by 241 (10 self)
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Short abstract, isn't it? P.A.C.S. numbers 05.20, 02.50, 87.10 1 Introduction Large Numbers "...the optimal tour displayed (see Figure 6) is the possible unique tour having one arc fixed from among 10 655 tours that are possible among 318 points and have one arc fixed. Assuming that one could possibly enumerate 10 9 tours per second on a computer it would thus take roughly 10 639 years of computing to establish the optimality of this tour by exhaustive enumeration." This quote shows the real difficulty of a combinatorial optimization problem. The huge number of configurations is the primary difficulty when dealing with one of these problems. The quote belongs to M.W Padberg and M. Grotschel, Chap. 9., "Polyhedral computations", from the book The Traveling Salesman Problem: A Guided tour of Combinatorial Optimization [124]. It is interesting to compare the number of configurations of realworld problems in combinatorial optimization with those large numbers arising in Cosmol...
Strategies for the parallel implementation of metaheuristics
 Essays and Surveys in Metaheuristics
, 2002
"... Abstract. Parallel implementationsof metaheuristicsappear quite naturally asan effective alternative to speed up the search for approximate solutions of combinatorial optimization problems. They not only allow solving larger problems or finding improved solutions with respect to their sequential cou ..."
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Abstract. Parallel implementationsof metaheuristicsappear quite naturally asan effective alternative to speed up the search for approximate solutions of combinatorial optimization problems. They not only allow solving larger problems or finding improved solutions with respect to their sequential counterparts, but they also lead to more robust algorithms. We review some trends in parallel computing and report recent results about linear speedups that can be obtained with parallel implementations using multiple independent processors. Parallel implementations of tabu search, GRASP, genetic algorithms, simulated annealing, and ant colonies are reviewed and discussed to illustrate the main strategies used in the parallelization of different metaheuristics and their hybrids. 1. Introduction. Although
An Evaluation of Parallel Simulated Annealing Strategies with Application to Standard Cell Placement
 IEEE Trans. on Comp. Aid. Design of Int. Cir. and Sys
, 1997
"... Simulated annealing, a methodology for solving combinatorial optimization problems, is a very computationally expensive algorithm, and as such, numerous researchers have undertaken efforts to parallelize it. In this paper, we investigate three of these parallel simulated annealing strategies when ap ..."
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Cited by 21 (1 self)
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Simulated annealing, a methodology for solving combinatorial optimization problems, is a very computationally expensive algorithm, and as such, numerous researchers have undertaken efforts to parallelize it. In this paper, we investigate three of these parallel simulated annealing strategies when applied to standard cell placement, specifically the TimberWolfSC placement tool. We have examined a parallel moves strategy, as well as two new approaches to parallel cell placement, multiple Markov chains and speculative computation. These algorithms have been implemented in ProperPLACE, our parallel cell placement application, as part of the ProperCAD II project. We have constructed ProperPLACE so that it is portable across a wide range of parallel architectures. Our parallel moves algorithm uses novel approaches to dynamic message sizing, message prioritization, and error control. We show that parallel moves and multiple Markov chains are effective approaches to parallel simulated annealin...
Parallel Simulated Annealing Strategies for VLSI Cell Placement
 IEEE Proc. 9 th Intl Conf. VLSI Design, pp 37
"... Simulated annealing based standard cell placement for VLSI designs has long been acknowledged as a computeintensive process, and as a result several research efforts have been undertaken to parallelize this algorithm. Most previous parallel approaches to cell placement annealing have used a paralle ..."
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Cited by 13 (3 self)
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Simulated annealing based standard cell placement for VLSI designs has long been acknowledged as a computeintensive process, and as a result several research efforts have been undertaken to parallelize this algorithm. Most previous parallel approaches to cell placement annealing have used a parallel moves approach. In this paper we investigate two new approaches that have been proposed for generalized parallel simulated annealing but have not been applied to the cell placement problem. Results are presented on the effectiveness of implementations of these algorithms when applied to the cell placement problem. We find that the first, multiple Markov chains, appears to be promising since it uses parallelism to obtain near linear speedups with no loss in quality. The second, speculative computation, while maintaining quality is not suitable since no speedups are achieved due to the specific nature of the cell placement problem. The two algorithms are compared with the parallel moves approach to parallel cell placement. 1
Parallel Algorithms for FPGA Placement
 In Majid Sarrafzadeh, Prithviraj Banerjee, and Kaushik Roy, editors, ACM Great Lakes Symposium on VLSI
, 2000
"... this paper is the first one to evaluate parallel placement algorithms for the FPGA placement application. We have investigated a range of parallel simulated annealing algorithms for FPGA placement. The parallel moves approach does not seem very promising due to loss of speedup tight by synchronizati ..."
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this paper is the first one to evaluate parallel placement algorithms for the FPGA placement application. We have investigated a range of parallel simulated annealing algorithms for FPGA placement. The parallel moves approach does not seem very promising due to loss of speedup tight by synchronization requirements and degradation in quality of result because of restricted moves. The second approach of area based partitioning provides better speedups and quality of solution. The speedup obtained is mainly due to reduction in synchronization. In the same direction the Markov chains approach reduces the synchronization 13
Parallel Simulated Annealing Algorithms in Global Optimization
 Journal of Global Optimization
, 2001
"... Abstract. Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel ..."
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Abstract. Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel Simulated Annealing (SA) algorithms and compare them on an extensive test bed used previously for the assessment of various solution approaches in global optimization. The parallel SA algorithms consist of various categories: the asynchronous approach where no information is exchanged among parallel runs and the synchronous approaches where solutions are exchanged using genetic operators, or where solutions are transmitted only occasionally, or where highly coupled synchronization is achieved at every iteration. One of these approaches, which occasionally applies partial information exchanges (controlled in terms of solution quality), provides particularly notable results for functions with vast search spaces of up to 400 dimensions. Previous attempts with other approaches, such as sequential SA, adaptive partitioning algorithms and clustering algorithms, to identify the global optima of these functions have failed without exception. Key words: Global optimization, Parallel simulated annealing 1.
Parallel Metaheuristics
, 1997
"... Metaheuristic parallel search methods  tabu search, simulated annealing and genetic algorithms, essentially  are reviewed, classified and examined not according to particular methodological characteristics, but following the unifying approach of the level of parallelization. It is hoped that by ..."
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Cited by 10 (5 self)
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Metaheuristic parallel search methods  tabu search, simulated annealing and genetic algorithms, essentially  are reviewed, classified and examined not according to particular methodological characteristics, but following the unifying approach of the level of parallelization. It is hoped that by examining the commonalities among parallel implementations across the field of metaheuristics, insights may be gained, trends may be discovered, and research challenges may be identified. Particular attention is paid to applications of parallel metaheuristics to transportation problems.
Mesh Partitioning for Efficient Use of Distributed Systems
 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
, 2002
"... Mesh partitioning for homogeneous systems has been studied extensively [2, 4, 14, 31, 36, 37, 41]; however, mesh partitioning for distributed systems is a relatively new area of research. To ensure efficient execution on a distributed system, the heterogeneities in the processor and network perfo ..."
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Cited by 8 (0 self)
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Mesh partitioning for homogeneous systems has been studied extensively [2, 4, 14, 31, 36, 37, 41]; however, mesh partitioning for distributed systems is a relatively new area of research. To ensure efficient execution on a distributed system, the heterogeneities in the processor and network performance must be taken into consideration in the partitioning process; equal size subdomains and small cut set size, which results from conventional mesh partitioning, are no longer the primary goals. In this paper, we address various issues related to mesh partitioning for distributed systems. These issues include the metric used to compare different partitions, efficiency of the application executing on a distributed system, and the advantage of exploiting heterogeneity in network performance. We present a tool called PART, for automatic mesh partitioning for distributed systems. The novel feature of PART is that it considers heterogeneities in the application and the distributed syst...
Simulated Annealing and Genetic Algorithms for Shape Detection
, 1996
"... this paper we consider the problem of recognizing simple geometric shapes in a picture corrupted by noise. The algorithmic techniques we use for its solution are simulated annealing, genetic algorithms and a constructive method based on noise filtering. Simulated annealing is a powerful stochastic t ..."
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Cited by 7 (0 self)
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this paper we consider the problem of recognizing simple geometric shapes in a picture corrupted by noise. The algorithmic techniques we use for its solution are simulated annealing, genetic algorithms and a constructive method based on noise filtering. Simulated annealing is a powerful stochastic technique for solving combinatorial optimization problems. One of the main drawbacks of simulated annealing is its high computational requirements. Because of this, a number of parallel implementations have been proposed [1, 5, 8, 10, 17, 23, 30]. In particular, in [10] some problem independent parallel implementations of simulated annealing have been described. Simulated annealing has been proposed to solve image recognition problems [6, 7, 28]. In particular, in [6] a parallel implementation of simulated annealing for the shape detection problem has been proposed. In this paper we present the results obtained using the farming implementation of simulated annealing as it was proposed in [10] for other applications. In Section 2 of this paper, the shape detection problem is formally defined and its representation in terms of a combinatorial optimization problem is described. In Section 3 the general simulated annealing algorithm is described together with some of the parallel implementations proposed for it. In Section 4 we describe a genetic algorithm for the shape detection problem. This algorithm is inherently parallel. In Section 5 we present a constructive heuristic for the shape detection problem which is based on a noise filter. Performance measurements presented in Section 6 for the different algorithms finish the paper. 2 The Shape Detection Problem
Parallel Algorithms for VLSI Circuit Extraction
, 1991
"... Parallel processing has recently become an extremely attractive and cost effective way of achieving orders of magnitude performance improvements in VLSI CAD applications. In this paper, we propose parallel algorithms to speed up the VLSI circuit extraction task. Given a VLSI layout as input, the pro ..."
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Cited by 7 (4 self)
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Parallel processing has recently become an extremely attractive and cost effective way of achieving orders of magnitude performance improvements in VLSI CAD applications. In this paper, we propose parallel algorithms to speed up the VLSI circuit extraction task. Given a VLSI layout as input, the problem of circuit extraction consists of determining the circuit connectivity and estimating the various electrical parameters such as the resistances of lines, capacitances of nodes, and dimensions of devices. Circuit extraction is a computationally intensive problem. The basic approach used in the parallel algorithms is the partitioning of a circuit into smaller regions, assigning each region to a processor and having the processors cooperate in performing the extraction procedures. We present a number of partitioning strategies that could be used. We have implemented the parallel algorithms on an Intel iPSC2 hypercube and an Encore 510 multimax shared memory multiprocessor. ADDRESS FOR CO...