Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
Models for Parallel and Distributed Computation: Theory, Algorithmic Techniques, and Applications
Models for Parallel and Distributed Computation: Theory, Algorithmic Techniques, and Applications
Multiobjective VLSI cell placement using distributed genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A parallel tabu search algorithm for optimizing multiobjective VLSI placement
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
An evaluation of parallel simulated annealing strategies with application to standard cell placement
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Simulated Evolution (SimE) is an evolutionary metaheuristic that has produced results comparable to well established stochastic heuristics such as SA, TS and GA, with shorter runtimes. However, for problems with a very large set of elements to optimize, such as in VLSI placement and routing, runtimes can still be very large and parallelization is an attractive option. Compared to other metaheuristics, parallelization of SimE has not been extensively explored. This paper presents a comprehensive set of parallelization approaches for SimE when applied to multiobjective VLSI cell placement problem. Each of these approaches are evaluated with respect to SimE characteristics and the constraints imposed by the problem instance. Conclusions drawn can be extended to parallelization of other SimE based optimization problems.