Reliability Algorithm Based on Adaptive Hybrid Particle Swarm Optimization and Simulated Annealing Algorithm

The sampling center of important sampling to predict structural or mechanical reliability is often computed by gradient-based iterative programs. However, there is often no convergence when calculating the highly nonlinear limitation equation. In this paper, a hybrid particle swarm optimization (PSO) and simulated annealing (SA) stochastic optimization algorithm is proposed and applied to the important sampling. The results of numerical examples confirm the theoretical analysis and show the characteristics of global search: fast convergence and high precision. This calculative method is effective and feasible to solve the problems in reliability.

I. Introduction

Reliability is the probability of a live connection between the source node and the sink node [1], [2]. It also is a measure of performance of systems. As systems grow more and more complex, the consequences of their unreliable behavior will become severe in terms of cost, effort, lives and so on, and the interest in assessing system reliability and the need to improve the reliability of products or systems have become very important. Monte Carlo method is often applied to reliability analysis and calculation [3], [4], however, the size of Monte Carlo (MC) sample is too large and the simulation efficiency is too low, especially when it comes to complex mechanical mechanism. The important sampling method is an improved MC method, and its computational efficiency is greatly improved [5].