An improved particle swarm optimization algorithm for reliability problems
| Autor: | Dexuan Zou, Steven Li, Liqun Gao, Peifeng Wu |
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| Přispěvatelé: | Wu, Peifeng, Gao, Liqun, Zou, Dexuan, Li, Steven |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Mathematical optimization
Reliability (computer networking) Normal Distribution Evolutionary algorithm Stability (learning theory) Swarm intelligence reliability problems Local optimum Convergence (routing) Industry Computer Simulation Electrical and Electronic Engineering Multi-swarm optimization Instrumentation Probability Mathematics convergence Applied Mathematics Reproducibility of Results Particle swarm optimization particle swarm optimization algorithm position updating Computer Science Applications Control and Systems Engineering Particulate Matter Electronics mutation Algorithm Algorithms |
| Popis: | 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. Refereed/Peer-reviewed |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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