School of Information Science and Engineering, Northeastern University, Shenyang, People's Republic of China.
ISA Trans. 2011 Jan;50(1):71-81. doi: 10.1016/j.isatra.2010.08.005. Epub 2010 Sep 20.
An improved particle swarm optimization (IPSO) algorithm is proposed to solve reliability problems in this paper. The IPSO designs two position updating strategies: In the early iterations, each particle flies and searches according to its own best experience with a large probability; in the late iterations, each particle flies and searches according to the fling experience of the most successful particle with a large probability. In addition, the IPSO introduces a mutation operator after position updating, which can not only prevent the IPSO from trapping into the local optimum, but also enhances its space developing ability. Experimental results show that the proposed algorithm has stronger convergence and stability than the other four particle swarm optimization algorithms on solving reliability problems, and that the solutions obtained by the IPSO are better than the previously reported best-known solutions in the recent literature.
本文提出了一种改进的粒子群优化（IPSO）算法来解决可靠性问题。该 IPSO 设计了两种位置更新策略：在早期迭代中，每个粒子根据其自身最佳经验以较大的概率进行飞行和搜索；在后期迭代中，每个粒子根据最成功粒子的抛掷经验以较大的概率进行飞行和搜索。此外，IPSO 在位置更新后引入了变异算子，不仅可以防止 IPSO 陷入局部最优，而且增强了其空间开发能力。实验结果表明，在解决可靠性问题方面，所提出的算法比其他四种粒子群优化算法具有更强的收敛性和稳定性，并且 IPSO 得到的解优于最近文献中先前报道的最佳已知解。
