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Parallel and interacting stochastic approximation annealing algorithms for global optimisation

机译：全局优化的并行和交互的随机近似退火算法

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摘要

We present the parallel and interacting stochastic approximation annealing (PISAA) algorithm, a stochastic simulation procedure for global optimisation, that extends and improves the stochastic approximation annealing (SAA) by using population Monte Carlo ideas. The efficiency of standard SAA algorithm crucially depends on its self-adjusting mechanism which presents stability issues in high dimensional or rugged optimisation problems. The proposed algorithm involves simulating a population of SAA chains that interact each other in a manner that significantly improves the stability of the self-adjusting mechanism and the search for the global optimum in the sampling space, as well as it inherits SAA desired convergence properties when a square-root cooling schedule is used. It can be implemented in parallel computing environments in order to mitigate the computational overhead. As a result, PISAA can address complex optimisation problems that it would be difficult for SAA to satisfactory address. We demonstrate the good performance of the proposed algorithm on challenging applications including Bayesian network learning and protein folding. Our numerical comparisons suggest that PISAA outperforms the simulated annealing, stochastic approximation annealing, and annealing evolutionary stochastic approximation Monte Carlo.
机译：我们提出了并行和交互的随机近似退火算法（PISAA），这是一种用于全局优化的随机模拟程序，它通过使用人口蒙特卡洛方法扩展和改进了随机近似退火（SAA）。标准SAA算法的效率主要取决于其自调整机制，该机制会在高维或严峻的优化问题中出现稳定性问题。所提出的算法涉及模拟相互交互的SAA链，以显着提高自我调整机制的稳定性和在采样空间中寻找全局最优值的方式，以及在继承SAA期望的收敛特性时进行仿真。使用平方根冷却时间表。可以在并行计算环境中实现它，以减轻计算开销。结果，PISAA可以解决SAA难以令人满意地解决的复杂优化问题。我们证明了所提出算法在包括贝叶斯网络学习和蛋白质折叠在内的挑战性应用中的良好性能。我们的数值比较表明，PISAA的性能优于模拟退火，随机逼近退火和退火演化随机逼近蒙特卡洛。

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