numerical optimization;
backtracking search algorithm;
sequential quadratic programming;
local search;
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摘要:
In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm (BSA) and sequential quadratic programming (SQP). BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.
机构:
Chinese Acad Sci, State Key Lab Sci & Eng Comp, Inst Computat Math & Sci Engn Comp, Acad Math & Syst Sci, Beijing 100190, Peoples R ChinaChinese Acad Sci, State Key Lab Sci & Eng Comp, Inst Computat Math & Sci Engn Comp, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Dai, Yu-Hong
Schittkowski, Klaus
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机构:
Univ Bayreuth, Dept Comp Sci, D-95440 Bayreuth, GermanyChinese Acad Sci, State Key Lab Sci & Eng Comp, Inst Computat Math & Sci Engn Comp, Acad Math & Syst Sci, Beijing 100190, Peoples R China