A study on scale factor in distributed differential evolution

Weber, Matthieu; Neri, Ferrante; Tirronen, Ville

doi:10.1016/j.ins.2011.02.008

Cited by 117 publications

(41 citation statements)

References 57 publications

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“…With the panmictic population structure, the original DE algorithms (Storn and Price 1995) are belong to this category, where any individuals can interact with any other one in the whole population. By introducing some structures into population, two main canonical kinds of structured population in DE could be found in literature, i.e., cellular DE (cDE) (Noman and Iba 2011;Noroozi et al 2011;Dorronsoro and Bouvry 2010;Liao et al 2015b) and distributed DE (dDE) (Weber et al 2011(Weber et al , 2010. DE with cellular topology (Noman and Iba 2011;Noroozi et al 2011;Dorronsoro and Bouvry 2010;Liao et al 2015b) Uses the cellular topology to define the configuration of neighborhood and select parents for mutation from the neighbors DE with ring topology-based mutation operators (Liao et al 2015a) Employs the ring topology to define the neighborhood and groups the neighbors to construct direction vector for mutation dDE with scale factor inheritance mechanism (Weber et al 2011) Incorporates the distributed population structure in DE and proposes the employment of multiple scale factor values within dDE structures…”

Section: Improving Mutation Operators With Neighborhood Informationmentioning

confidence: 99%

Neighborhood guided differential evolution

et al. 2016

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Differential evolution (DE) relies mainly on its mutation mechanism to guide its search. Generally, the parents involved in mutation are randomly selected from the current population. Although such a mutation strategy is easy to use, it is inefficient for solving complex problems. Hence, how to utilize population information to further enhance the search ability of the mutation operator has become one of the most salient and active topics in DE. To address this issue, a new DE framework with the concept of index-based neighborhood, is proposed in this study. The proposed framework is named as neighborhood guided DE (NGDE). In NGDE, a neighborhood guided selection (NGS) is introduced to guide the mutation process by extracting the promising search directions with the neighborhood information. NGS includes four main operators: neighborhood construction, neighbors grouping, two-level neighbors ranking, and parents selection. With these four operators, NGS can utilize the topology and fitness information of population simultaneously. To evaluate the effectiveness of the proposed approach, NGS is applied to several original and advanced DE algorithms. Experimental results have shown that NGDE generally outperforms most of the corresponding DE algorithms on different kinds of optimization problems.

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Section: Improving Mutation Operators With Neighborhood Informationmentioning

confidence: 99%

Neighborhood guided differential evolution

et al. 2016

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“…In these DE variants, the individuals for mutation are selected according to a neighbor list constructed from the population topologies. Two main canonical kinds of structured population in DE is employed in literature, i.e., cellular DE (cDE) (Noman and Iba 2011;Noroozi et al 2011;Dorronsoro and Bouvry 2010) and distributed DE (dDE) (Weber et al 2011(Weber et al , 2010Neri et al 2011). In De Falco et al (2014, the impact of several network topologies on the performance of distributed differential evolution was investigated.…”

Section: Related Workmentioning

confidence: 99%

“…3, many attempts with neighborhood or/and direction information of population are effective to guide the search of DE. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance (Noman and Iba 2011;Noroozi et al 2011;Dorronsoro and Bouvry 2010;Weber et al 2011Weber et al , 2010Neri et al 2011;Das et al 2009;Omran et al 2006Omran et al , 2009Cai et al 2012;Wang and Xiang 2008;Fan and Lampinen 2003;Cai and Wang 2013;Bi and Xiao 2011). However, in most DE algorithms, on the one hand, the base and difference vectors are always selected randomly or locally.…”

Section: Motivationsmentioning

confidence: 99%

Cellular direction information based differential evolution for numerical optimization: an empirical study

et al. 2015

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Differential evolution (DE) is a well-known evolutionary algorithm which has been successfully applied in many scientific and engineering fields. In most DE algorithms, the base and difference vectors for mutation are randomly selected from the current population. That is, the useful population information cannot be fully exploited to guide the search of DE through mutation. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance. Therefore, to alleviate this drawback and improve the performance of DE, a novel DE algorithm with a directional mutation based on cellular topology is proposed in this study. This proposed algorithm is named as Cellular Direction Information based DE (DE-CDI). In DE-CDI, the cellular topology is employed first to define a neighborhood for each individual of population and then the direction information based on the neighborhood is incorporated into the mutation operator. In this way, DE-CDI not only utilizes the neighborhood information to exploit the regions of better individuals and accelerate convergence, but also introduces the direction information to guide the search to the promising area. To evaluate the performance of the proposed method, DE-CDI is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of DE-CDI for most DE algorithms studied.

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“…Since, as shown in [2], [10], [11], and [12], DE is characterized by a limited amount of search moves, modifications of the original scheme can lead to a performance enhancement. These modifications, in some cases, are not major in terms of programming effort but can still lead to significant improvements, see [13] and [14].…”

Section: Introductionmentioning

confidence: 99%

Differential evolution schemes for speech segmentation: A comparative study

Iliya

Neri

Menzies

et al. 2014

2014 IEEE Symposium on Differential Evolution (SDE)

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Abstract-This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback. The functioning of the signal processing technique has been optimized by selecting the parameters of the model. The optimization has been carried out by testing and comparing multiple Differential Evolution implementations, including a standard one, a memetic one, and a controlled randomized one. Numerical results have also been compared with a famous and efficient swarm intelligence algorithm. For the given problem, Differential Evolution schemes appear to display a very good performance as they can quickly reach a high quality solution. The binomial crossover appears, for the given problem, beneficial with respect to the exponential one. The controlled randomization appears to be the best choice in this case. The overall optimized system proved to segment well the speech utterances and efficiently detect its uninteresting parts.

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A study on scale factor in distributed differential evolution

Cited by 117 publications

References 57 publications

Neighborhood guided differential evolution

Neighborhood guided differential evolution

Cellular direction information based differential evolution for numerical optimization: an empirical study

Differential evolution schemes for speech segmentation: A comparative study

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