A new evaluation strategy-based interval optimization algorithm and its simulation analysis

This paper presents a new interval optimization algorithm (ESIA) combining interval algorithm with evolution strategy in bionics., to improve the search efficiency and make the accelerated tool constructed easier comparing with the traditional interval algorithm (IA), hence it can be applied to high...

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Vydáno v:Chinese Control and Decision Conference s. 887 - 890
Hlavní autoři: Guan Shou-ping, Han Yu-huan, Peng Xiu-yuan, Lu Chuang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2017
Témata:
Acceleration
Algorithm design and analysis
Evaluation strategy
Global optimization
Interval optimization algorithm
Optimization
Reliability
Sociology
Truncation selection
Upper bound
ISSN:1948-9447
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Abstract This paper presents a new interval optimization algorithm (ESIA) combining interval algorithm with evolution strategy in bionics., to improve the search efficiency and make the accelerated tool constructed easier comparing with the traditional interval algorithm (IA), hence it can be applied to high dimensional optimization problems better. The ESIA employed the evaluation strategy to construct accelerated tool, which can be used to cut off the interval elements with low probability of including the global optimal solution, and a reliable upper bound is provided to prune intervals and the calculation of the algorithm is reduced. Meanwhile, a new splitting rule is proposed to make the reliable interval, which probably contains the global optimal solution, have more chance to split, so as to further improve the search efficiency. The numerical experiments on several typical test functions show that the ESIA is more efficient than traditional IA.
AbstractList This paper presents a new interval optimization algorithm (ESIA) combining interval algorithm with evolution strategy in bionics., to improve the search efficiency and make the accelerated tool constructed easier comparing with the traditional interval algorithm (IA), hence it can be applied to high dimensional optimization problems better. The ESIA employed the evaluation strategy to construct accelerated tool, which can be used to cut off the interval elements with low probability of including the global optimal solution, and a reliable upper bound is provided to prune intervals and the calculation of the algorithm is reduced. Meanwhile, a new splitting rule is proposed to make the reliable interval, which probably contains the global optimal solution, have more chance to split, so as to further improve the search efficiency. The numerical experiments on several typical test functions show that the ESIA is more efficient than traditional IA.
Author Han Yu-huan
Peng Xiu-yuan
Guan Shou-ping
Lu Chuang
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  surname: Han Yu-huan
  fullname: Han Yu-huan
  organization: Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
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  surname: Peng Xiu-yuan
  fullname: Peng Xiu-yuan
  organization: LAAS, Inf. Center, Shenyang, China
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  surname: Lu Chuang
  fullname: Lu Chuang
  organization: LAAS, Inf. Center, Shenyang, China
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Snippet This paper presents a new interval optimization algorithm (ESIA) combining interval algorithm with evolution strategy in bionics., to improve the search...
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SubjectTerms Acceleration
Algorithm design and analysis
Evaluation strategy
Global optimization
Interval optimization algorithm
Optimization
Reliability
Sociology
Truncation selection
Upper bound
Title A new evaluation strategy-based interval optimization algorithm and its simulation analysis
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