The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pada 2019, algoritma baharu sepenuhnya, evolusi sfera (SE), telah dicadangkan. Gaya carian serba baharu dalam SE telah terbukti mempunyai keupayaan carian yang kukuh. Untuk memanfaatkan SE, kami mencadangkan kaedah baru yang dipanggil kaedah keturunan tangga (LD) untuk meningkatkan strategi kemas kini populasi SE dan selepas itu mencadangkan algoritma carian evolusi sfera tangga (LSE). Dengan bilangan lelaran yang semakin meningkat, julat individu induk yang layak untuk menghasilkan anak secara beransur-ansur berubah daripada keseluruhan populasi kepada individu optimum semasa, dengan itu meningkatkan keupayaan penumpuan algoritma. Keputusan percubaan pada fungsi penanda aras IEEE CEC2017 menunjukkan keberkesanan LSE.
Haichuan YANG
University of Toyama
Shangce GAO
University of Toyama
Rong-Long WANG
University of Fukui
Yuki TODO
Kanazawa University
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Salinan
Haichuan YANG, Shangce GAO, Rong-Long WANG, Yuki TODO, "A Ladder Spherical Evolution Search Algorithm" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 3, pp. 461-464, March 2021, doi: 10.1587/transinf.2020EDL8102.
Abstract: In 2019, a completely new algorithm, spherical evolution (SE), was proposed. The brand new search style in SE has been proved to have a strong search capability. In order to take advantage of SE, we propose a novel method called the ladder descent (LD) method to improve the SE' population update strategy and thereafter propose a ladder spherical evolution search (LSE) algorithm. With the number of iterations increasing, the range of parent individuals eligible to produce offspring gradually changes from the entire population to the current optimal individual, thereby enhancing the convergence ability of the algorithm. Experiment results on IEEE CEC2017 benchmark functions indicate the effectiveness of LSE.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8102/_p
Salinan
@ARTICLE{e104-d_3_461,
author={Haichuan YANG, Shangce GAO, Rong-Long WANG, Yuki TODO, },
journal={IEICE TRANSACTIONS on Information},
title={A Ladder Spherical Evolution Search Algorithm},
year={2021},
volume={E104-D},
number={3},
pages={461-464},
abstract={In 2019, a completely new algorithm, spherical evolution (SE), was proposed. The brand new search style in SE has been proved to have a strong search capability. In order to take advantage of SE, we propose a novel method called the ladder descent (LD) method to improve the SE' population update strategy and thereafter propose a ladder spherical evolution search (LSE) algorithm. With the number of iterations increasing, the range of parent individuals eligible to produce offspring gradually changes from the entire population to the current optimal individual, thereby enhancing the convergence ability of the algorithm. Experiment results on IEEE CEC2017 benchmark functions indicate the effectiveness of LSE.},
keywords={},
doi={10.1587/transinf.2020EDL8102},
ISSN={1745-1361},
month={March},}
Salinan
TY - JOUR
TI - A Ladder Spherical Evolution Search Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 461
EP - 464
AU - Haichuan YANG
AU - Shangce GAO
AU - Rong-Long WANG
AU - Yuki TODO
PY - 2021
DO - 10.1587/transinf.2020EDL8102
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2021
AB - In 2019, a completely new algorithm, spherical evolution (SE), was proposed. The brand new search style in SE has been proved to have a strong search capability. In order to take advantage of SE, we propose a novel method called the ladder descent (LD) method to improve the SE' population update strategy and thereafter propose a ladder spherical evolution search (LSE) algorithm. With the number of iterations increasing, the range of parent individuals eligible to produce offspring gradually changes from the entire population to the current optimal individual, thereby enhancing the convergence ability of the algorithm. Experiment results on IEEE CEC2017 benchmark functions indicate the effectiveness of LSE.
ER -