A New Adaptive Hybrid Algorithm for Large-Scale Global Optimization

Ninglei Fan, Yuping Wang*, Junhua Liu, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


Large-scale global optimization (LSGO) problems are one of most difficult optimization problems and many works have been done for this kind of problems. However, the existing algorithms are usually not efficient enough for difficult LSGO problems. In this paper, we propose a new adaptive hybrid algorithm (NAHA) for LSGO problems, which integrates the global search, local search and grouping search and greatly improves the search efficiency. At the same time, we design an automatic resource allocation strategy which can allocate resources to different optimization strategies automatically and adaptively according to their performance and different stages. Furthermore, we propose a self-learning parameter adjustment scheme for the parameters in local search and grouping search, which can automatically adjust parameters. Finally, the experiments are conducted on CEC 2013 LSGO competition benchmark test suite and the proposed algorithm is compared with several state-of-the-art algorithms. The experimental results indicate that the proposed algorithm is pretty effective and competitive.

Original languageEnglish
Title of host publicationAdvances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
EditorsHuchuan Lu, Huajin Tang, Zhanshan Wang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030227951
Publication statusPublished - 26 Jun 2019
Event16th International Symposium on Neural Networks, ISNN 2019 - Moscow, Russian Federation
Duration: 10 Jul 201912 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11554 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Neural Networks, ISNN 2019
Country/TerritoryRussian Federation

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Global search
  • Grouping search
  • Large scale global optimization
  • Local search
  • Parameter automatical adjustment
  • Resource allocation
  • Self-learning


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