Differential Evolution With Two-Level Parameter Adaptation

Yu, Wei; Shen, Meie; Chen, Wei-Neng; Zhan, Zhi-Hui; Gong, Yue-Jiao; Lin, Ying; Liu, Ou; Zhang, Mengjie

doi:10.1109/tcyb.2013.2279211

Cited by 189 publications

(49 citation statements)

References 38 publications

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“…EC is a nonconventional optimization paradigm, inspired by the mechanisms of natural evolution and behaviors of living organisms [Zhang et al 2011c]. In general, EC algorithms include Evolutionary Algorithms (EAs) such as the Genetic Algorithm (GA), swarm intelligence algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), and other nature-inspired algorithms [Yu et al 2014;Zhan and Zhang 2010;Zhan et al 2012;Zhang et al 2014b]. Recent work has shown an emerging trend in the use of EC algorithms for improved effectiveness and efficiency in complex optimization systems [Li et al 2015d].…”

Section: Cloud Resource Scheduling Algorithmsmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Section: Cloud Resource Scheduling Algorithmsmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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“…In the future, the adaptive or self-adaptive parameter control techniques, e.g., Qin et al (2009), Yu et al (2014 and Sarker et al (2014), will be studied to alleviate this problem.…”

Section: Sensitivity To Neighborhood Radius (R)mentioning

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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“…Parameter adaptation methods have been widely used in DE [12]- [14], [16], [34], [36], which have proved to be important for DE to perform well on different kinds of problems. In [32], a bare-bone DE is proposed to solve the parameter setting problem of DE in a different way.…”

Section: B Differential Evolution As Efficient Eamentioning

confidence: 99%

Fast Micro-Differential Evolution for Topological Active Net Optimization

Zhan

Gong

et al. 2016

IEEE Trans. Cybern.

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This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a "best improvement local search" (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm.

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Differential Evolution With Two-Level Parameter Adaptation

Cited by 189 publications

References 38 publications

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Neighborhood guided differential evolution

Fast Micro-Differential Evolution for Topological Active Net Optimization

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