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
본 논문에서는 검색 프로세스에 따라 각 입자에 대한 적응형 검색 전략을 제공하는 적응형 배율 변환 기반 입자 군집 최적화 프로그램(AMT-PSO)을 제시합니다. 배율 변환은 사물을 훨씬 더 선명하게 보기 위해 볼록 렌즈를 사용하는 것에서 영감을 얻은 간단하지만 매우 강력한 메커니즘입니다. 이 변환의 핵심은 관심 영역 주변에 돋보기를 설정하여 관심 영역을 보다 주의깊고 정확하게 검사할 수 있도록 하는 것입니다. 입자 떼의 인구 분포 정보를 활용하는 진화 요인은 각 차원의 각 입자에 대한 배율 배율을 적응적으로 조정하는 지표로 사용됩니다. 또한, 섭동 기반 엘리트 학습 전략을 활용하여 떼의 최고의 입자가 로컬 최적을 탈출하고 잠재적으로 더 나은 공간을 탐색하도록 돕습니다. AMT-PSO는 15개의 단일 모드 및 다중 모드 벤치마크 기능으로 평가되었습니다. AMT-PSO의 적응형 배율 변환 메커니즘과 엘리트 학습 전략의 효과를 연구합니다. 결과는 적응형 확대 변환 메커니즘이 벤치마크 테스트 기능의 네 가지 범주에 대한 수렴 속도 및 솔루션 정확도 측면에서 제안된 AMT-PSO에 주요 기여를 제공한다는 것을 보여줍니다.
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부
Junqi ZHANG, Lina NI, Chen XIE, Ying TAN, Zheng TANG, "AMT-PSO: An Adaptive Magnification Transformation Based Particle Swarm Optimizer" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 4, pp. 786-797, April 2011, doi: 10.1587/transinf.E94.D.786.
Abstract: This paper presents an adaptive magnification transformation based particle swarm optimizer (AMT-PSO) that provides an adaptive search strategy for each particle along the search process. Magnification transformation is a simple but very powerful mechanism, which is inspired by using a convex lens to see things much clearer. The essence of this transformation is to set a magnifier around an area we are interested in, so that we could inspect the area of interest more carefully and precisely. An evolutionary factor, which utilizes the information of population distribution in particle swarm, is used as an index to adaptively tune the magnification scale factor for each particle in each dimension. Furthermore, a perturbation-based elitist learning strategy is utilized to help the swarm's best particle to escape the local optimum and explore the potential better space. The AMT-PSO is evaluated on 15 unimodal and multimodal benchmark functions. The effects of the adaptive magnification transformation mechanism and the elitist learning strategy in AMT-PSO are studied. Results show that the adaptive magnification transformation mechanism provides the main contribution to the proposed AMT-PSO in terms of convergence speed and solution accuracy on four categories of benchmark test functions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.786/_p
부
@ARTICLE{e94-d_4_786,
author={Junqi ZHANG, Lina NI, Chen XIE, Ying TAN, Zheng TANG, },
journal={IEICE TRANSACTIONS on Information},
title={AMT-PSO: An Adaptive Magnification Transformation Based Particle Swarm Optimizer},
year={2011},
volume={E94-D},
number={4},
pages={786-797},
abstract={This paper presents an adaptive magnification transformation based particle swarm optimizer (AMT-PSO) that provides an adaptive search strategy for each particle along the search process. Magnification transformation is a simple but very powerful mechanism, which is inspired by using a convex lens to see things much clearer. The essence of this transformation is to set a magnifier around an area we are interested in, so that we could inspect the area of interest more carefully and precisely. An evolutionary factor, which utilizes the information of population distribution in particle swarm, is used as an index to adaptively tune the magnification scale factor for each particle in each dimension. Furthermore, a perturbation-based elitist learning strategy is utilized to help the swarm's best particle to escape the local optimum and explore the potential better space. The AMT-PSO is evaluated on 15 unimodal and multimodal benchmark functions. The effects of the adaptive magnification transformation mechanism and the elitist learning strategy in AMT-PSO are studied. Results show that the adaptive magnification transformation mechanism provides the main contribution to the proposed AMT-PSO in terms of convergence speed and solution accuracy on four categories of benchmark test functions.},
keywords={},
doi={10.1587/transinf.E94.D.786},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - AMT-PSO: An Adaptive Magnification Transformation Based Particle Swarm Optimizer
T2 - IEICE TRANSACTIONS on Information
SP - 786
EP - 797
AU - Junqi ZHANG
AU - Lina NI
AU - Chen XIE
AU - Ying TAN
AU - Zheng TANG
PY - 2011
DO - 10.1587/transinf.E94.D.786
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2011
AB - This paper presents an adaptive magnification transformation based particle swarm optimizer (AMT-PSO) that provides an adaptive search strategy for each particle along the search process. Magnification transformation is a simple but very powerful mechanism, which is inspired by using a convex lens to see things much clearer. The essence of this transformation is to set a magnifier around an area we are interested in, so that we could inspect the area of interest more carefully and precisely. An evolutionary factor, which utilizes the information of population distribution in particle swarm, is used as an index to adaptively tune the magnification scale factor for each particle in each dimension. Furthermore, a perturbation-based elitist learning strategy is utilized to help the swarm's best particle to escape the local optimum and explore the potential better space. The AMT-PSO is evaluated on 15 unimodal and multimodal benchmark functions. The effects of the adaptive magnification transformation mechanism and the elitist learning strategy in AMT-PSO are studied. Results show that the adaptive magnification transformation mechanism provides the main contribution to the proposed AMT-PSO in terms of convergence speed and solution accuracy on four categories of benchmark test functions.
ER -