title: An improved particle swarm optimization algorithm for reliability problems.
abstract: 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.
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang, People's Republic of China.
authors.forename: Peifeng
authors.initials: P
authors.lastname: Wu
authors.forename: Liqun
authors.initials: L
authors.lastname: Gao
authors.forename: Dexuan
authors.initials: D
authors.lastname: Zou
authors.forename: Steven
authors.initials: S
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004581:Electronics; D007221:Industry / Q000295:instrumentation*; D016011:Normal Distribution; D052638:Particulate Matter; D011336:Probability; D015203:Reproducibility of Results
chemical_list: D052638:Particulate Matter