Adaptive Multi-scale Quantum Harmonic Oscillator Algorithm Based on Evolutionary Strategy

This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search...

Full description

Saved in:
Bibliographic Details
Published in:2020 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors: Ye, Xinggui, Wang, Peng
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2020
Subjects:
adaptive mechanism
Benchmark testing
Convergence
differential evolution
Evolutionary strategy
Harmonic analysis
multi-scale quantum harmonic oscillator algorithm
Oscillators
population-based optimization
Sociology
Statistics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search scale, the searching step decreases too fast at the later stage of the evolution and is more likely to suffer premature convergence and stagnation. To improve the convergence performance, an adaptive attenuation mechanism of scaling is proposed to dynamically adjust the exploration and exploitation properties. Evolutionary strategies such as selection, crossover and DE/rand/1 mutation are implemented in the proposed algorithm to enhance the exploration and exploitation abilities. Experimental results evaluated on several unimodal and multimodal benchmark functions indicate the significant improvement of the proposed algorithm to the original MQHOA. Meanwhile, the experimental results compared with several state-of-the-art optimizers show the superiority or competitiveness of the proposed algorithm.
AbstractList This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical optimization. Since the original Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) utilizes a fixed contraction factor to narrow the search scale, the searching step decreases too fast at the later stage of the evolution and is more likely to suffer premature convergence and stagnation. To improve the convergence performance, an adaptive attenuation mechanism of scaling is proposed to dynamically adjust the exploration and exploitation properties. Evolutionary strategies such as selection, crossover and DE/rand/1 mutation are implemented in the proposed algorithm to enhance the exploration and exploitation abilities. Experimental results evaluated on several unimodal and multimodal benchmark functions indicate the significant improvement of the proposed algorithm to the original MQHOA. Meanwhile, the experimental results compared with several state-of-the-art optimizers show the superiority or competitiveness of the proposed algorithm.
Author Wang, Peng
Ye, Xinggui
Author_xml – sequence: 1
  givenname: Xinggui
  surname: Ye
  fullname: Ye, Xinggui
  organization: University of Chinese Academy of Sciences,Beijing,China
– sequence: 2
  givenname: Peng
  surname: Wang
  fullname: Wang, Peng
  organization: Southwest Minzu University,School of Computer Science and Technology,Chengdu,China
BookMark eNotj0FLwzAYQCPowU1_gQj5A635kjRNjrVMJ0yGqAdPI02_zkDajDYd7N87cKd3e7y3INdDHJCQR2A5ADNP9aqWWjGVc8ZZbkAXpdBXZAEl16AMN3BLfqrWHpI_In2fQ_LZ5GxA-jHbIc09Xduxj4N3dDs5H4JNcaRV2MfRp9-ePtsJWxoHujrGMCcfBzue6GcabcL96Y7cdDZMeH_hkny_rL7qdbbZvr7V1SbzADplYFyDynXIrCybVkjnnMFWykY4LjutBZ5DATkHiwwKrrBQVqFoHEhuGrEkD_9ej4i7w-j7c8Xuciv-ABMUUMw
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/CEC48606.2020.9185738
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
Accès UT - IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Statistics
EISBN 1728169291
9781728169293
EndPage 8
ExternalDocumentID 9185738
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i118t-19cbe6cfe0a47bd34ccc9ed44b3c24f883e2911e221ae01526e56a6e3bc1429b3
IEDL.DBID RIE
IngestDate Mon Jul 08 05:38:35 EDT 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-19cbe6cfe0a47bd34ccc9ed44b3c24f883e2911e221ae01526e56a6e3bc1429b3
PageCount 8
ParticipantIDs ieee_primary_9185738
PublicationCentury 2000
PublicationDate 2020-July
PublicationDateYYYYMMDD 2020-07-01
PublicationDate_xml – month: 07
  year: 2020
  text: 2020-July
PublicationDecade 2020
PublicationTitle 2020 IEEE Congress on Evolutionary Computation (CEC)
PublicationTitleAbbrev CEC
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.7675703
Snippet This paper proposes a novel adaptive multi-scale quantum harmonic oscillator algorithm based on evolutionary strategies (AMQHOA-ES) for global numerical...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms adaptive mechanism
Benchmark testing
Convergence
differential evolution
Evolutionary strategy
Harmonic analysis
multi-scale quantum harmonic oscillator algorithm
Oscillators
population-based optimization
Sociology
Statistics
Title Adaptive Multi-scale Quantum Harmonic Oscillator Algorithm Based on Evolutionary Strategy
URI https://ieeexplore.ieee.org/document/9185738
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La8JAEB5UevDUh5a-2UOPjSa7ee3RiuLJWmjBnmQfk1aoiaSJ4L_vbhIshV56CCwhsPDNZr-dZL5vAO6ZCsxhKIkcaSXufhBRRyRcO0xy6iaB62pMqmYT0XweL5d80YKHgxYGEaviMxzYYfUvX2eqtJ_KhtwaF7G4De0oCmutViPK8Vw-HE_GtqOSrTug7qB59lfTlIozpsf_m-0E-j_iO7I40MoptDA9g649FNaeyj14G2mxtdsUqeSzzpfBGclzaUAqN2Qm8o31uyVPht1MkE1STUaf71m-Lj425NGwliZZSia7ZtGJfE8aj9p9H16nk5fxzGlaJDhrkxkUjseVxFAl6Ao_kpr5SimO2vclU9RP4pghNdsZUuoJNMxPQwxCESKTyjNMJNk5dNIsxQsgUoeeiLmQ5vJFILhJpXRgXl-NPGFCXkLPYrTa1i4Yqwaeq79vX0PXhqEubL2BTpGXeAtHamewyu-q0H0DYyeeTg
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFA5zCu7JyybezYOPdmuT9JLHOTYmzjlhwnwauZzqwK2jroP9e5O2TARffCiEUgh8J82X057vOwjdUuWbw1AcOtJK3JkfEkfEXDtUcuLGvutqiPNmE-FwGE0mfFRBd1stDADkxWfQtMP8X75OVGY_lbW4NS6i0Q7a9RkjbqHWKmU5nstbnW7H9lSylQfEbZZP_2qbkrNG7-B_8x2ixo_8Do-2xHKEKrA4RjV7LCxclevora3F0m5UOBfQOl8GacAvmYEpm-O-SOfW8RY_G34zYTZpNW5_vifpbPUxx_eGtzROFri7LpedSDe4dKndNNBrrzvu9J2ySYIzM7nByvG4khCoGFzBQqkpU0px0IxJqgiLo4gCMRsaEOIJMNxPAvADEQCVyjNcJOkJqi6SBZwiLHXgiYgLaS4mfMFNMqV98wJr4DEV8gzVLUbTZeGDMS3hOf_79g3a74-fBtPBw_DxAtVsSIoy10tUXaUZXKE9tTa4pdd5GL8ByrKhlQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2020+IEEE+Congress+on+Evolutionary+Computation+%28CEC%29&rft.atitle=Adaptive+Multi-scale+Quantum+Harmonic+Oscillator+Algorithm+Based+on+Evolutionary+Strategy&rft.au=Ye%2C+Xinggui&rft.au=Wang%2C+Peng&rft.date=2020-07-01&rft.pub=IEEE&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FCEC48606.2020.9185738&rft.externalDocID=9185738