[1]LIU Changfen,HAN Honggui,QIAO Junfei.Self-adaptive DE algorithm via generalized opposition-based learning[J].CAAI Transactions on Intelligent Systems,2015,10(1):131-137.[doi:10.3969/j.issn.1673-4785.201310068]
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Self-adaptive DE algorithm via generalized opposition-based learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 1 Page number: 131-137 Column: 学术论文—人工智能基础 Public date: 2015-03-25
Title:
Self-adaptive DE algorithm via generalized opposition-based learning
Author(s):
LIU Changfen1; 2;  HAN Honggui1; 2;  QIAO Junfei1; 2
1. College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing 100124, China
Keywords:
differential evolution; optimization; generalized opposition-based learning; convergencespeed; accuracy; highdimension; initialization
CLC:
TP18;O224
DOI:
10.3969/j.issn.1673-4785.201310068
Abstract:
The problem related to defects of complex parameter setting and difficult selection of mutation strategies existing in the differential evolution (DE) algorithm when solving high-dimensional optimization problem is studied. This paper proposed a new self-adaptive DE algorithm based on generalized opposition-based learning (SDE-GOBL). The generalized opposition-based learning (GOBL) is utilized for the adjustment of initiation strategy on multi-strategy self-adaptive DE (SaDE) algorithm. The corresponding reverse points of each candidate solution are figured out. In addition, the necessary optimal initial population is selected among the candidate solutions and its reverse points. Next, the self-adaptive mutation, hybridization and selection operations are conducted. Finally, nine standard test functions provided in CEC2005 International Competition are applied for demonstrating SDE-GOBL algorithm. The result showed that the algorithm has fast convergence speed and high solution precision.
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