Parallel and Interacting Stochastic Approximation Annealing algorithms for global optimisation

TL;DR

The paper introduces PISAA, an advanced parallel stochastic optimization algorithm that improves upon SAA by using a population of interacting chains, leading to faster convergence and better exploration in complex, high-dimensional problems.

Contribution

The paper proposes PISAA, a novel parallel and interacting stochastic approximation annealing algorithm that enhances convergence and exploration over traditional SAA using population-based strategies.

Findings

PISAA outperforms simulated annealing and SAA in high-dimensional problems.

PISAA demonstrates faster convergence and better exploration in complex scenarios.

The algorithm is suitable for parallel computing environments.

Abstract

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 standard SAA algorithm guarantees convergence to the global minimum when a square-root cooling schedule is used; however the efficiency of its performance depends crucially on its self-adjusting mechanism. Because its mechanism is based on information obtained from only a single chain, SAA may present slow convergence in complex optimisation problems. The proposed algorithm involves simulating a population of SAA chains that interact each other in a manner that ensures significant improvement of the self-adjusting mechanism and better exploration of the sampling space. Central to the proposed algorithm are the ideas of (i) recycling information from the whole population of Markov chains to design a more accurate/stable self-adjusting mechanism and (ii) incorporating more advanced proposals, such as crossover operations, for the exploration of the sampling space. PISAA presents a significantly improved performance in terms of convergence. PISAA can be implemented in parallel computing environments if available. 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 especially in high dimensional or rugged scenarios.