Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set

Viktorin, Adam; Pluháček, Michal; Šenkeřík, Roman

doi:10.1109/cec.2016.7744404

Cited by 38 publications

(17 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research of randomization issues and insights into the inner dynamic of metaheuristic algorithms was many times addressed as essential and beneficial. The results presented here support the approach for multi-chaotic generators [24] or ensemble systems, where we can profit from the combined/selective population diversity (i.e., exploration/exploitation) tendencies, sequencing-based either stronger or moderate progress towards the function extreme, all given by the smart combination of multi-randomization schemes.…”

Section: Resultssupporting

confidence: 73%

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Šenkeřík

Viktorin

Zelinka

et al. 2018

mendel

Self Cite

View full text Add to dashboard Cite

This research represents a detailed insight into the modern and popular hybridization of deterministic chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete deterministic chaotic systems, as the chaotic pseudorandom number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10D and 15 test functions from the CEC 2015 benchmark, further statistically analyzed.

show abstract

Section: Resultssupporting

confidence: 73%

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Šenkeřík

Viktorin

Zelinka

et al. 2018

mendel

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, in MDE_ pBX (Islam et al 2012) the Lehmer mean is replaced by a pow mean. A different algorithm was proposed by Tanabe et al (Tanabe and Fukunaga 2013), stated as SHADE, which maintains memories of F and CR values calculated as weighted Lehmer mean and weighted arithmetic mean of successful F and CR values from the last generation An improved version of SHADE was proposed in 2016 (Viktorin et al 2016), in which a multichaotic framework is used to select the parents that will be used during the mutation phase.…”

Section: Adaptation Of Parametersmentioning

confidence: 99%

“…Secondly, RAM-JAPDE have been compared with other well-known DE algorithms: ODE (Rahnamayan et al 2006), JADE (Zhang and Sanderson 2009), SHADE (Tanabe and Fukunaga 2013), jDE (Brest et al 2006), IDDE (Sun et al 2018), MC-SHADE (Viktorin et al 2016), η_CODE (Deng et al 2019) and sinDE (Draa et al 2015). The parameters used in the algorithms are the same as described in their respective papers and are listed below:…”

Section: Comparison Of Ram-japde With Other Adaptive Differential Evomentioning

confidence: 99%

See 1 more Smart Citation

Adaptive differential evolution with a new joint parameter adaptation method

León

Xiong

2020

Soft Comput

View full text Add to dashboard Cite

Differential evolution (DE) is a population-based metaheuristic algorithm that has been proved powerful in solving a wide range of real-parameter optimization tasks. However, the selection of the mutation strategy and control parameters in DE is problem dependent, and inappropriate specification of them will lead to poor performance of the algorithm such as slow convergence and early stagnation in a local optimum. This paper proposes a new method termed as Joint Adaptation of Parameters in DE (JAPDE). The key idea lies in dynamically updating the selection probabilities for a complete set of pairs of parameter generating functions based on feedback information acquired during the search by DE. Further, for mutation strategy adaptation, the Rank-Based Adaptation (RAM) method is utilized to facilitate the learning of multiple probability distributions, each of which corresponds to an interval of fitness ranks of individuals in the population. The coupling of RAM with JAPDE results in the new RAM-JAPDE algorithm that enables simultaneous adaptation of the selection probabilities for pairs of control parameters and mutation strategies in DE. The merit of RAM-JAPDE has been evaluated on the benchmark test suit proposed in CEC2014 in comparison to many well-known DE algorithms. The results of experiments demonstrate that the proposed RAM-JAPDE algorithm outperforms or is competitive to the other related DE variants that perform mutation strategy and control parameter adaptation, respectively.

show abstract

“…The hybridization of the DE with other softcomputing fields has shown to be valuable both for analysis and for performance improvement -DE and complex networks [13], [14], [15], DE and chaotic generators [16], [17]. Therefore, in this paper, another softcomputing field, cluster analysis, is used for the analysis of population development in the Db_SHADE algorithm.…”

Section: Introductionmentioning

confidence: 99%

Clustering Analysis of the Population in Db_SHADE Algorithm

Viktorin¹,

Šenkeřík²,

Pluháček³

et al. 2018

mendel

Self Cite

View full text Add to dashboard Cite

This paper provides an analysis of the population clustering in a novel Success-History based Adaptive Differential Evolution algorithm with Distance based adaptation (Db_SHADE) in order to analyze the exploration and exploitation abilities of the algorithm. The comparison with the original SHADE algorithm is performed on the CEC2015 benchmark set in two dimensional settings (10D and 30D). The clustering analysis helps to answer the question about prolonged exploration phase of the Db_SHADE algorithm. Possible future research directions are drawn in the discussion and conclusion.

show abstract

Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set

Cited by 38 publications

References 15 publications

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Adaptive differential evolution with a new joint parameter adaptation method

Clustering Analysis of the Population in Db_SHADE Algorithm

Contact Info

Product

Resources

About