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Robust multi‐user detection based on hybrid Grey wolf optimization
Author(s) -
Sun Xiyan,
Fan Zhuo,
Ji Yuanfa,
Wang Shouhua,
Yan Suqing,
Wu Sunyong,
Fu Qiang,
Ghazali Kamarul Hawari
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5273
Subject(s) - differential evolution , meta optimization , computer science , algorithm , mathematical optimization , optimization algorithm , impulse (physics) , genetic algorithm , optimization problem , artificial intelligence , mathematics , machine learning , physics , quantum mechanics
Summary The search for an effective nature‐inspired optimization technique has certainly continued for decades. This work proposes a novel robust multi‐user detection algorithm based on Grey wolf optimization and differential evolution algorithm to overcome the problem of high bit error rate (BER) in multi‐user detection under an impulse noise environment. The simulation results show that the iteration times of the multi‐user detector based on the proposed algorithm is less than those of genetic algorithm, differential evolution algorithm, Grey wolf optimization algorithm, salp swarm algorithm, grasshopper optimisation algorithm, and whale optimization algorithm with the lowerst BER value.