DE-RCO: Rotating Crossover Operator With Multiangle Searching Strategy for Adaptive Differential Evolution

Differential evolution (DE) is confirmed as a simple yet efficacious methodology to solve practical optimization problems. In this paper, we develop a new rotating crossover operator (RCO), to improve the optimization performance by utilizing multiangle searching strategy-based RCO. The proposed cro...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.2970-2983
Main Authors: Deng, Li-Bao, Wang, Sha, Qiao, Li-Yan, Zhang, Bao-Quan
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Crossovers
Differential evolution (DE)
Evolutionary algorithms
Evolutionary computation
multiangle searching strategy
Operators (mathematics)
Optimization
Performance enhancement
rotating crossover operator
Rotation
rotation angles and radii
Search methods
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Summary:Differential evolution (DE) is confirmed as a simple yet efficacious methodology to solve practical optimization problems. In this paper, we develop a new rotating crossover operator (RCO), to improve the optimization performance by utilizing multiangle searching strategy-based RCO. The proposed crossover scheme, different from conventional crossover operators, can generate trial vectors in control of the self-adaptive crossover parameter and rotation control vectors, which obey Lévy distribution. More specifically, trial vectors are generated diversely within circle regions around donor vectors and target vectors, by multiplying the rotation control vectors and difference of donor and target vectors. Rotation angles and radii are adjusted along with angles and moduli of the rotation control vectors. The proposed RCO operator can be easily applied to crossover strategies of other DE variants with minor changes. In order to verify the efficiency and generality of the algorithm, the proposed RCO scheme is respectively applied to the conventional DE variants and a state-of-the-art algorithm JADE, denoted as JADE-RCO. Further comparison experiments of JADE-RCO and other five efficient DE variants are conducted to confirm the superiority of the improved algorithm JADE-RCO. Series of experiments on a set of test functions in CEC 2013 demonstrate that the DE-RCO shows excellent performance in convergence rate and optimization ability comparing with classic and advanced evolutionary algorithms and it improves the performance of the original algorithms by 57%-96%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2786347