Relaxing Synchronization in Distributed Simulated Annealing

Simulated annealing is an attractive, but expensive, heuristic for approximating the solution to combinatorial optimization problems. Since simulated annealing is a general purpose method, it can be applied to the broad range of NP-complete problems such as the traveling salesman problem, graph theory, and cell placement with a careful control of the cooling schedule. Attempts to parallelize simulated annealing, particularly on distributed memory multicomputers, are hampered by the algorithm’s requirement of a globally consistent system state. In a multicomputer, maintaining the global state S involves explicit message traffic and is a critical performance bottleneck. One way to mitigate this bottleneck is to amortize the overhead of these state updates over as many parallel state changes as possible. By using this technique, errors in the actual cost C(S) of a particular state S will be introduced into the annealing process. This dissertation places analytically derived bounds on the cost error in order to assure convergence to the correct result. The resulting parallel Simulated Annealing algorithm dynamically changes the frequency of global updates as a function of the annealing control parameter, i.e. temperature. Implementation results on an Intel iPSC/2 are reported

Optimization of patch antennas via multithreaded simulated annealing based design exploration

In this paper, we present a new software framework for the optimization of the design of microstrip patch antennas. The proposed simulation and optimization framework implements a simulated annealing algorithm to perform design space exploration in order to identify the optimal patch antenna design. During each iteration of the optimization loop, we employ the popular MEEP simulation tool to evaluate explored design solutions. To speed up the design space exploration, the software framework is developed to run multiple MEEP simulations concurrently. This is achieved using multithreading to implement a manager-workers execution strategy. The number of worker threads is the same as the number of cores of the computer that is utilized. Thus, the computational runtime of the proposed software framework enables effective design space exploration. Simulations demonstrate the effectiveness of the proposed software framework

Variable Annealing Length and Parallelism in Simulated Annealing

In this paper, we propose: (a) a restart schedule for an adaptive simulated annealer, and (b) parallel simulated annealing, with an adaptive and parameter-free annealing schedule. The foundation of our approach is the Modified Lam annealing schedule, which adaptively controls the temperature parameter to track a theoretically ideal rate of acceptance of neighboring states. A sequential implementation of Modified Lam simulated annealing is almost parameter-free. However, it requires prior knowledge of the annealing length. We eliminate this parameter using restarts, with an exponentially increasing schedule of annealing lengths. We then extend this restart schedule to parallel implementation, executing several Modified Lam simulated annealers in parallel, with varying initial annealing lengths, and our proposed parallel annealing length schedule. To validate our approach, we conduct experiments on an NP-Hard scheduling problem with sequence-dependent setup constraints. We compare our approach to fixed length restarts, both sequentially and in parallel. Our results show that our approach can achieve substantial performance gains, throughout the course of the run, demonstrating our approach to be an effective anytime algorithm.Comment: Tenth International Symposium on Combinatorial Search, pages 2-10. June 201

Stochastic optimization methods for extracting cosmological parameters from CMBR power spectra

The reconstruction of the CMBR power spectrum from a map represents a major computational challenge to which much effort has been applied. However, once the power spectrum has been recovered there still remains the problem of extracting cosmological parameters from it. Doing this involves optimizing a complicated function in a many dimensional parameter space. Therefore efficient algorithms are necessary in order to make this feasible. We have tested several different types of algorithms and found that the technique known as simulated annealing is very effective for this purpose. It is shown that simulated annealing is able to extract the correct cosmological parameters from a set of simulated power spectra, but even with such fast optimization algorithms, a substantial computational effort is needed.Comment: 7 pages revtex, 3 figures, to appear in PR

Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima

The optimization of three problems with high dimensionality and many local minima are investigated under five different optimization algorithms: DIRECT, simulated annealing, Spall’s SPSA algorithm, the KNITRO package, and QNSTOP, a new algorithm developed at Indiana University