Journal of Northeastern University Natural Science ›› 2019, Vol. 40 ›› Issue (11): 1521-1526.DOI: 10.12068/j.issn.1005-3026.2019.11.001

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Interval Multi-objective Particle Swarm Optimization Algorithm and Its Application

GUAN Shou-ping, ZOU Li-fu, ZHANG Jing-jing   

  1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2018-12-06 Revised:2018-12-06 Online:2019-11-15 Published:2019-11-05
  • Contact: GUAN Shou-ping
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Abstract: An interval multi-objective particle swarm optimization(IMOPSO)algorithm was proposed to solve the optimization problem of interval variables under multi-objectives. A dominant relationship between two intervals was defined based on interval credibility. Normalization method and interval crowding distance were used to sort the Pareto-optimal solutions. And an archiving mechanism was set up to save the Pareto optimal set in the external memory. Then, an interval neural network(INN)employing the IMOPSO to train was proposed for the unknown-but-bounded(UBB)errors modeling problem, which can be suited for the two situations of the error bounds that is either known or unknown. The first-order uncertain system was taken as an example to verify the proposed method, and the simulation results validated the effectiveness.

Key words: interval multi-objective optimization, interval particle swarm optimization, interval neural network, unknown but bounded(UBB), first-order uncertain system

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