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
2019년에는 완전히 새로운 알고리즘인 SE(Spherical Evolution)가 제안되었습니다. SE의 새로운 검색 스타일은 강력한 검색 기능을 가지고 있음이 입증되었습니다. SE의 장점을 활용하기 위해 SE' 모집단 업데이트 전략을 개선하는 Ladder Descent(LD) 방법이라는 새로운 방법을 제안하고 이후 Ladder Spherical Evolution Search(LSE) 알고리즘을 제안합니다. 반복 횟수가 증가함에 따라 자손을 생산할 수 있는 부모 개체의 범위가 전체 개체군에서 현재 최적 개체로 점진적으로 변경되어 알고리즘의 수렴 능력이 향상됩니다. IEEE CEC2017 벤치마크 기능에 대한 실험 결과는 LSE의 효율성을 나타냅니다.
Haichuan YANG
University of Toyama
Shangce GAO
University of Toyama
Rong-Long WANG
University of Fukui
Yuki TODO
Kanazawa University
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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
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@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},}
부
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 -