Differential evolution optimization with time-frame strategy adaptation

In differential evolution (DE) research, many successful empirical guidelines in selecting appropriate trial vector generation strategies and control parameter values for various problems have been investigated. The comprehensive exploration of the experience can be an effective way to develop an ad...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2017-06, Vol.21 (11), p.2991-3012
Main Author: Wang, Shih-Chang
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Artificial Intelligence
Computational Intelligence
Control
Engineering
Evolutionary computation
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Optimization
Parameters
Robotics
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Summary:In differential evolution (DE) research, many successful empirical guidelines in selecting appropriate trial vector generation strategies and control parameter values for various problems have been investigated. The comprehensive exploration of the experience can be an effective way to develop an advanced DE variant. In this paper, an improved DE approach with time-frame strategy adaptation called the time-frame adaptive differential evolution (TFADE) is proposed. It employs diverse trial vector generation strategies with various control parameter values that can be adaptively determined to generate promising solutions and dynamically adjusted to deal with premature convergence during evolution, according to successful experience over a period of preceding generations called the time frame. In the experimental study, TFADE is compared with 4 commonly used conventional DEs, 3 outstanding state-of-the-art adaptive DEs, and 2 novel non-DE approaches, evaluated by a test suite of 25 benchmark functions. The experimental results show that the performance of TFADE is significantly superior to these competitors.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1982-0