Generalised opposition-based differential evolution: an experimental study

Author(s):  
Hui Wang ◽  
Shahryar Rahnamayan ◽  
Sanyou Zeng
2019 ◽  
Vol 50 ◽  
pp. 100453 ◽  
Author(s):  
Rafał Biedrzycki ◽  
Jarosław Arabas ◽  
Dariusz Jagodziński

Author(s):  
G. Jeyakumar ◽  
C. Shanmugavelayutham

The Differential Evolution (DE) is a well known Evolutionary Algorithm (EA), and is popular for its simplicity. Several novelties have been proposed in research to enhance the performance of DE. This paper focuses on demonstrating the performance enhancement of DE by implementing some of the recent ideas in DE’s research viz. Dynamic Differential Evolution (dDE), Multiple Trial Vector Differential Evolution (mtvDE), Mixed Variant Differential Evolution (mvDE), Best Trial Vector Differential Evolution (btvDE), Distributed Differential Evolution (diDE) and their combinations. The authors have chosen fourteen variants of DE and six benchmark functions with different modality viz. Unimodal Separable, Unimodal Nonseparable, Multimodal Separable, and Multimodal Nonseparable. On analyzing distributed DE and mixed variant DE, a novel mixed-variant distributed DE is proposed whereby the subpopulations (islands) employ different DE variants to cooperatively solve the given problem. The competitive performance of mixed-variant distributed DE on the chosen problem is also demonstrated. The variants are well compared by their mean objective function values and probability of convergence.


Author(s):  
Takashi Ishimizu ◽  
◽  
Kiyoharu Tagawa

In this paper, a new Differential Evolution (DE) that has multiple populations, or islands, is proposed. The proposed DE is called Structured Differential Evolution (StDE). In order to generate a new individual from the current population, various characteristic strategies have been proposed for DE. However, the performances of these strategies depend on the kind of the optimization problem. The proposed StDE uses different strategies in respective islands. Therefore, it can be expected that the proposed StDE is effective for a wide range of optimization problems. Although various networks topologies among islands are reported for island-based evolutionary algorithms, the most popular ones, namely the ring network and the torus network, are employed by StDE. Furthermore, in order to enhance the performance of proposed StDE, various migration policies are examined in two kinds of networks though a variety of benchmark problems.


2011 ◽  
Vol 2 (2) ◽  
pp. 58-81 ◽  
Author(s):  
G. Jeyakumar ◽  
C. Shanmugavelayutham

The Differential Evolution (DE) is a well known Evolutionary Algorithm (EA), and is popular for its simplicity. Several novelties have been proposed in research to enhance the performance of DE. This paper focuses on demonstrating the performance enhancement of DE by implementing some of the recent ideas in DE’s research viz. Dynamic Differential Evolution (dDE), Multiple Trial Vector Differential Evolution (mtvDE), Mixed Variant Differential Evolution (mvDE), Best Trial Vector Differential Evolution (btvDE), Distributed Differential Evolution (diDE) and their combinations. The authors have chosen fourteen variants of DE and six benchmark functions with different modality viz. Unimodal Separable, Unimodal Nonseparable, Multimodal Separable, and Multimodal Nonseparable. On analyzing distributed DE and mixed variant DE, a novel mixed-variant distributed DE is proposed whereby the subpopulations (islands) employ different DE variants to cooperatively solve the given problem. The competitive performance of mixed-variant distributed DE on the chosen problem is also demonstrated. The variants are well compared by their mean objective function values and probability of convergence.


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