Scale factor inheritance mechanism in distributed differential evolution

Weber, Matthieu; Tirronen, Ville; Neri, Ferrante

doi:10.1007/s00500-009-0510-5

Cited by 91 publications

(47 citation statements)

References 50 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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“…Moreover, the exploration and exploitation degrees of subpopulations on the same side are gradual. Other heterogeneous island dEAs can be found in [138,139].…”

Section: Island Modelmentioning

confidence: 99%

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

Gong

Chen

Zhan

et al. 2015

Applied Soft Computing

340

132

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a b s t r a c tThe increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

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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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Scale factor inheritance mechanism in distributed differential evolution

Cited by 91 publications

References 50 publications

Neighborhood guided differential evolution

Neighborhood guided differential evolution

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

Differential evolution schemes for speech segmentation: A comparative study

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