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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Vydané v:Chinese Control and Decision Conference s. 887 - 890
Hlavní autori: Guan Shou-ping, Han Yu-huan, Peng Xiu-yuan, Lu Chuang
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.05.2017
Predmet:
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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Shrnutí: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.
ISSN:1948-9447
DOI:10.1109/CCDC.2017.7978645