Performance comparison of self-adaptive and adaptive differential evolution algorithms

Brest, Janez; Bošković, Borko; Greiner, S.; Žumer, V.; Maučec, Mirjam Sepesy

doi:10.1007/s00500-006-0124-0

Cited by 191 publications

(105 citation statements)

References 15 publications

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“…In order to avoid the manual tuning of DE control parameters, a self-adaptation scheme [23] has been implemented. The values of F and C R are encoded into the individuals, which enter the optimization procedure, and randomly initialized within the intervals F min , F max and C R min , C R max , respectively.…”

Section: Standard Differential Evolutionmentioning

confidence: 99%

Preliminary Capture Trajectory Design for Europa Tomography Probe

Federici

Zavoli

Colasurdo

2018

International Journal of Aerospace Engineering

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The objective of this work is the preliminary design of a low-ΔV transfer from an initial elliptical orbit around Jupiter into a final circular orbit around the moon Europa. This type of trajectory represents an excellent opportunity for a low-cost mission to Europa, accomplished through a small orbiter, as in the proposed Europa Tomography Probe mission, a European contribution to NASA's Europa Multiple-Flyby Mission (or Europa Clipper). The mission strategy is based on the v ∞ leveraging concept, and the use of resonant orbits to exploit multiple gravity-assist from the moon. Possible sequences of resonant orbits are selected with the help of the Tisserand graph. Suitable trajectories are provided by an optimization code based on the parallel running of several differential evolution algorithms. Different solutions are finally compared in terms of propellant consumption and flight time.

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Section: Standard Differential Evolutionmentioning

confidence: 99%

Preliminary Capture Trajectory Design for Europa Tomography Probe

Federici

Zavoli

Colasurdo

2018

International Journal of Aerospace Engineering

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“…They also used SaDE on constrained problems [9]. In [2] and [25] strategy adaptation techniques similar to SaDE are also used to enhance DE performance. To the best of our knowledge, the study on adaptive strategy selection in DE is still scarce.…”

Section: Introductionmentioning

confidence: 99%

Adaptive strategy selection in differential evolution

Gong

Fialho

Cai

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization. Different strategies have been proposed for the offspring generation; but the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, the probability matching technique is employed in DE to autonomously select the most suitable strategy while solving the problem. Four credit assignment methods, that update the known performance of each strategy based on the relative fitness improvement achieved by its recent applications, are analyzed. To evaluate the performance of our approach, thirteen widely used benchmark functions are used. Experimental results confirm that our approach is able to adaptively choose the suitable strategy for different problems. Compared to classical DE algorithms and to a recently proposed adaptive scheme (SaDE), it obtains better results in most of the functions, in terms of the quality of the final results and convergence speed.

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“…MaxG to remain user defined [6,7,8,9]. The latest contribution to a growing literature on parameter adaptation is in [9] which proposed the adaptive variant we discuss here.…”

Section: Q Crmentioning

confidence: 99%

“…The latest contribution to a growing literature on parameter adaptation is in [9] which proposed the adaptive variant we discuss here. Other Adaptive DE versions can be found in [6,7,8].…”

Section: Q Crmentioning

confidence: 99%

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An Adaptive Differential Evolution Algorithm Applied to Highway Network Capacity Optimization

Koh

Advances in Soft Computing

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Abstract. Differential Evolution (DE) is a simple heuristic for global search. However, it is sensitive to certain control parameters and may not perform well if these parameters are not adjusted to suit the problems being optimized. Recent research has reported on methods to endogenously tune these control parameters during the search process. In this work, we develop and apply two DE variants as solution algorithms for continuous network design problems and illustrate with examples from the highway transportation literature.

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Performance comparison of self-adaptive and adaptive differential evolution algorithms

Cited by 191 publications

References 15 publications

Preliminary Capture Trajectory Design for Europa Tomography Probe

Preliminary Capture Trajectory Design for Europa Tomography Probe

Adaptive strategy selection in differential evolution

An Adaptive Differential Evolution Algorithm Applied to Highway Network Capacity Optimization

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