Distributed optimal resource allocation of second‐order multiagent systems

Deng, Zhenhua; Liang, Shu; Yu, Wenwu

doi:10.1002/rnc.4233

Cited by 56 publications

(26 citation statements)

References 38 publications

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“…where Remark 5. When the w i is an identity matrix, the problem (14) is simplified to the resource allocation problem. 29 The allocation decisions are made to minimize the sum of all the agents' local objective functions while satisfying both the global network resource constraint and the local allocation feasibility constraints.…”

Section: Problem Formulationmentioning

confidence: 99%

“…As mentioned above, the resource allocation problem can be viewed as distributed optimization with equality constraints. Compared with the existing optimal resource allocation, 14,29,39 the proposed proximal-gradient algorithm is not only smooth but can solve the NSO for second-order multiagent systems. This section is divided into two parts: first, smooth proximal-gradient algorithm is proposed for nonsmooth resource allocation problem of single-integrator multiagent systems.…”

Section: Distributed Continuous-time Algorithm Based On Proximal Opermentioning

confidence: 99%

“…In the study of distributed optimization, the global objective is to optimize the global cost function with a sum of convex local cost functions subject to some local constraints. Specially, distributed optimization for multiagent systems has been widely developed due to the wide range of applications in science and engineering such as distributed optimal consensus, 12,13 resource allocation, 14,15 and distributed optimization localization, 16 just to name a few. Nonsmooth optimization (NSO) refers to the general problem of minimizing (or maximizing) functions that are typically not differentiable.…”

Section: Introductionmentioning

confidence: 99%

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Distributed proximal‐gradient algorithms for nonsmooth convex optimization of second‐order multiagent systems

Wang

Chen

Zeng

et al. 2020

Intl J Robust & Nonlinear

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This article studies the distributed nonsmooth convex optimization problems for second-order multiagent systems. The objective function is the summation of local cost functions which are convex but nonsmooth. Each agent only knows its local cost function, local constraint set, and neighbor information. By virtue of proximal operator and Lagrangian methods, novel continuous-time distributed proximal-gradient algorithms with derivative feedback are proposed to solve the nonsmooth convex optimization for the consensus and resource allocation of multiagent systems, respectively. With the proposed algorithms, both the consensus and resource allocation problems are solved. Moreover, the system can converge to the optimal solution. Finally, simulation examples are given to illustrate the effectiveness of the proposed algorithms.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Distributed Continuous-time Algorithm Based On Proximal Opermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distributed proximal‐gradient algorithms for nonsmooth convex optimization of second‐order multiagent systems

Wang

Chen

Zeng

et al. 2020

Intl J Robust & Nonlinear

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“…In terms analysis of migration can be found connection with simulations of the process of architecture and development of tourism with help multiagent systems (Bai 2013). Other suitable examples of using multi-agent systems can be found in (Yang et al 2019), (Deng et al 2018), (Valbuena et al 2008), and (Charnley et al 2019).…”

Section: Germanymentioning

confidence: 99%

Local Extremes of Selected Industry 4.0 Indicators in the European Space—Structure for Autonomous Systems

Botlíková

Botlík

2020

JRFM

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In the past, the social and economic impacts of industrial revolutions have been clearly identified. The current Fourth Industrial Revolution (Industry 4.0) is characterized by robotization, digitization, and automation. This will transform the production processes, but also the services or financial markets. Specific groups of people and activities may be replaced by new information technologies. Changes represent an extreme risk of economic instability and social change. The authors described available published sources and selected a group of indicators related to Industry 4.0. The indicators were divided into five groups and summarized by negative or positive impact. The indicators were analyzed by precedence analysis. Extremes in the geographical dislocation of factor values were found. Furthermore, spatial dependencies in the distribution of these extremes were found by calculating multiple (long) precedencies. European countries were classified according to individual groups of indicators. The results were compared with the real values of the indicators. The indicated extremes and their distribution will allow to predict changes in the behavior of the population given by changes in the socio-economic environment. The behavior of the population can be described by the behavior of autonomous systems on selected infrastructure. The paper presents research related to the creation of a multiagent model for the prediction of spatial changes in population distribution induced by Industry 4.0.

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“…In terms analysis of migration can be found connection with simulations of the process of architecture and development of tourism with help multiagent systems [19]. Other suitable examples of using multi-agent systems and simulation can be found in [20], [21], [22], [23].…”

Section: Current State Of Knowledgementioning

confidence: 99%

Movement of autonomous systems after selected infrastructure as a globalization effect induced by initiate Industry 4.0

Botlík¹

2020

SHS Web Conf.

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The replacement of specialized, highly sophisticated human work with systems using artificial intelligence is one of the features of the 4th Industrial Revolution known as Industry 4.0. The upcoming innovations and transformations of production processes, the digitized of information and the automation will bring about changes at the social level. These are mainly changes in the company’s behavior. There is a significant risk for specific groups of people, especially those that can be replaced by new information technologies. In the context of the current sixth wave of globalization, new forms of migration of people and capital can be expected related to transnational nature of productive activities, the global form of communications and information. In the context of socio-economic structures, an individual is confronted with a set of factors. The aim of an individual’s behavior is usually to change his localization with respect to the values of the preferred socioeconomic variables, such as availability of work, safety, air quality, etc. On the other hand, the position of an individual will influence the values of socio-economic variables. Behavior can be simulated using multiagent systems. The paper informs about the first phase of the research. Local maxima of factor values were identified.

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Distributed optimal resource allocation of second‐order multiagent systems

Cited by 56 publications

References 38 publications

Distributed proximal‐gradient algorithms for nonsmooth convex optimization of second‐order multiagent systems

Distributed proximal‐gradient algorithms for nonsmooth convex optimization of second‐order multiagent systems

Local Extremes of Selected Industry 4.0 Indicators in the European Space—Structure for Autonomous Systems

Movement of autonomous systems after selected infrastructure as a globalization effect induced by initiate Industry 4.0

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