Abstract:

In recent years, a broad class of stochastic metaheuristics, such as Tabu search, simulated annealing, genetic algorithm, particle swarm optimization, ant colony optimization etc. has been applied for reliability optimization problems. In this paper a particle swarm optimization algorithm is presented. Then, the performance of the proposed algorithm is tested on some complex engineering optimization problems. They are three well-known complex reliability optimization problems. Finally, the results are compared with those given by several well-known methods. Numerical experiments demonstrate that the proposed method is promising and the results obtained by proposed algorithm are either superior or comparable to the previously best known results presented in literature for reliability optimization of complex systems in terms of computation time as well as solution quality.