Heterogeneous Strategy Particle Swarm Optimization

Wei, Du; Ying, Wen; Yan, Gang; Zhu, Yongtian; Cao, Xianbin

doi:10.1109/tcsii.2016.2595597

Cited by 50 publications

(23 citation statements)

References 24 publications

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“…Considering these results, it becomes clear that the optimization [536][537][538][539] of the subsidy policy is indeed a difficult yet very important issue, which can mean the difference between a successful and an unsuccessful vaccination campaign. For the optimal outcome, it is important to carefully consider the mechanisms of individual decision-making, as well as to actually test on a representative sample how people respond to different external incentives in realistic situations.…”

Section: External Incentive Programsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: External Incentive Programsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…Engineers are always looking into behaviour, mechanics, physiology of living systems to uncover novel principles of distributed sensing, information processing and decision making that could be adopted in development of future and emergent computing paradigms, architectures and implementations (Deng et al 2015;Du et al 2015Du et al , 2016Zhang et al 2013a). For example, Lei and Guo (2013) developed a modified artificial bee colony (MABC) algorithm to solve the job shop scheduling problem with lot streaming and transportation.…”

Section: Introductionmentioning

confidence: 99%

Physarum solver: a bio-inspired method for sustainable supply chain network design problem

et al. 2017

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A supplier of products and services aims to minimize the capacity investment cost and the operational cost incurred by unwanted byproducts, e.g. carbon dioxide emission. In this paper, we consider a sustainable supply chain network design problem, where the capacity and the product flow along each link are design variables. We formulate it as a multi-criteria optimization problem. A bio-inspired algorithm is developed to tackle this problem. We illustrate how to design a sustainable supply chain network in three steps. First, we develop a generalized model inspired by the foraging behaviour of slime mould Physarum polycephalum to handle the network optimization with multiple sinks. Second, we propose a strategy to update the link cost iteratively, thus making the Physarum model to converge to a user equilibrium. Third, we perform an equivalent operation to transform a system optimum problem into a corresponding user equilibrium problem so that it is solvable in the Physarum model. The efficiency of the proposed algorithm is illustrated with numerical examples.

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“…A wide range of systems in nature and society can be described as complex networks, such as the World Wide Web, neural networks and air transportation networks, etc. In the past decades, the study of complex networks has given rise to great achievements in many fields [1,2,3,4], such as network modeling [5,6,7], cascading failures [8,9,10,11,12], evolutionary games [13,14,15,16], optimization [17,18,19] and traffic dynamics [20,21,22] and so on.…”

Section: Introductionmentioning

confidence: 99%

Efficient calculation of the robustness measureRfor complex networks

Chen

Lordan

et al. 2017

Physica A: Statistical Mechanics and its Applications

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Article publicat / Published paper: Hong, C.; et al. Efficient calculation of the robustness measure R for complex networks. AbstractIn a recent work [Proc. Natl. Acad. Sci. USA, 108 (2011) 3838], Schneider et al. proposed a new measure R for network robustness, where the value of R is calculated within the entire process of malicious node attacks. In this paper, we present an approach to improve the calculation efficiency of R, in which a computationally efficient robustness measure R ′ is introduced when the fraction of failed nodes reaches to a critical threshold q c . Simulation results on three different types of network models and three real networks show that these networks all exhibit a computationally efficient robustness measure R ′ . The relationships between R ′ and the network size N and the network average degree ⟨k⟩ are also explored. It is found that the value of R ′ decreases with N while increases with ⟨k⟩. Our results would be useful for improving the calculation efficiency of network robustness measure R for complex networks.

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Heterogeneous Strategy Particle Swarm Optimization

Cited by 50 publications

References 24 publications

Statistical physics of vaccination

Statistical physics of vaccination

Physarum solver: a bio-inspired method for sustainable supply chain network design problem

Efficient calculation of the robustness measureRfor complex networks

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