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研究非相同元件并联的单目标-单约束S-P网络系统可靠性优化问题。选择常用的群体智能算法,包括模拟退火算法、蚁群算法、遗传算法、粒子群优化算法对模型求解。通过模拟仿真发现:几种算法给出的最优解情况不尽相同,蚁群算法求解精度高、收敛率100%,但执行时间长、解的编码复杂、解空间搜索复杂;粒子群优化算法收敛性较好,最优解的收敛率比较高;遗传算法搜索到最优解数量较少,收敛率比较低;模拟退火算法也能收敛到最优解,但收敛率较低,优点是容易实现。选择算法时,要依据问题的规模、时间、收敛率与精度进行选择。
This paper studies the reliability optimization of single-constrained S-P networks with single-target and parallel components. Select common group intelligence algorithms, including simulated annealing algorithm, ant colony algorithm, genetic algorithm, particle swarm optimization algorithm to solve the model. The simulation results show that the optimal solutions are different for several algorithms. The ant colony algorithm has the advantages of high precision and 100% convergence rate. However, the execution time is long, the encoding of the solution is complicated and the solution space search is complicated. The particle swarm optimization The convergence rate is better, the convergence rate of the optimal solution is relatively high; the optimal number of solutions is less, the convergence rate is lower; the simulated annealing algorithm can also converge to the optimal solution, but the convergence rate is low, the advantage is easy achieve. Select the algorithm, according to the scale of the problem, time, convergence rate and accuracy of choice.