Survey of evolutionary computation methods in social agent-based modeling studies

Révay, Peter; Cioffi‐Revilla, Claudio

doi:10.1007/s42001-017-0003-8

Cited by 6 publications

(3 citation statements)

References 54 publications

(126 reference statements)

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“…The usage of evolutionary algorithms is not only in the domain of network but in other domains as well [38]. Different algorithms are also used with the fuzzy based system to solve the complex problems [39]. Optimization is also becoming very well-used in the different domains of networks to make the performance more efficient [40].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

et al. 2019

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A network of wirelessly connected vehicles by using any mean of connectivity is termed as the Internet of Vehicle (IoV). Managing this type of network is a challenging task. Clustering is a technique to efficiently manage resources in this type of network. In a cluster, all inter/intra cluster communication is managed by a cluster head (CH). Load on each CH, the lifetime of the cluster and the total number of clusters in a network are some parameters to measure the efficiency of the network. In this paper, a novel technique based on moth flame clustering algorithm for IoV (MFCA-IoV) is proposed. Moth flame optimizer is a nature-inspired algorithm. MFCA-IoV generates optimized clusters for robust transmission and is evaluated experimentally with renowned techniques. These techniques are Grey-Wolf-optimizationbased method used for the clustering called as GWOCNETs, multi-objective particle-swarm-optimization (MOPSO), clustering algorithm based on Ant colony optimization for vehicular ad-hoc networks termed as CACONET and comprehensive learning particle-swarm-optimization (CLPSO). To assess the comparative efficiency of these algorithms, numerous experiments are performed. The parameters like network gridsize, number of nodes, speed, direction, and transmission-range of the nodes are considered for optimized clustering. The results indicate, MFCA-IoV is showing 73% nodes, which are not selected as a cluster head while existing techniques are providing 57%, 50%, 51%, and 58% for GWOCNETs, CLPSO, MOPSO, and CACONET, respectively. Hence, lesser the nodes are selected as CH, the more optimal result will be considered. INDEX TERMS Internet of Vehicle (IoV), vehicular ad-hoc networks (VANETs), intelligent transportation system (ITS), Ant-colony-optimization (ACO), particle swarm optimization (PSO), MFO; clustering, meta-heuristic algorithms, population-based algorithm.

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

et al. 2019

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“…The guiding principle of soft computing is to explore the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost that are not handled with conventional hard computing. Initially, soft computing is composed of three main branches: fuzzy systems [20,21], evolutionary computation [22,23], artificial neural computing [24]. Up to now, many new methods or techniques have been proposed for imprecision, uncertainty and partial truth, which are belong to soft computing.…”

Section: Soft Computingmentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

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The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

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“…As a bottom-up modeling approach, the agent-based modeling (ABM) technique offers the possibility to reveal the non-linear relationship between the global state and the interaction of the agents [32]. It is an effective tool for studying complex adaptive systems and their emergence [33][34][35], which provides a reliable theoretical aid for decision makers. This method has been widely used in various fields such as supply chain management, environmental management, ecology, and sociology [36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Research on the Influencing Factors for the Use of Green Building Materials through the Number Growth of Construction Enterprises Based on Agent-Based Modeling

Sun

Zeng

et al. 2022

Sustainability

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As the concept of green building is increasingly promoted worldwide, the use of green building materials has also attracted great attention. In order to improve the utilization rate of green building materials in construction projects, this paper tries to explore influencing factors based on the method of agent-based modeling (ABM), which is well-suited to the study of complex systems and their emergent behaviors. There are two types of agents being set, named, respectively, greedy construction enterprises and selective construction enterprises. Meanwhile, the construction unit’s intention to use green building materials is treated as the criterion. Two types of construction enterprises compete for projects to obtain profits. In order to reveal the mechanism of the evolutionary path of the macro system, the movement step, project unit profit, project cost and expansion cost are adopted as the independent variables and the number of enterprises is the dependent variable. Then, the experiment was performed with the NetLogo simulation platform. The simulation results show that, it is not beneficial for the selective construction enterprises when the movement step in selecting projects is too long and the project units are too profitable. However, when the project cost and expansion cost become higher, there is a significant advantage for selective construction enterprises to go for a long-term development. Therefore, after a comprehensive evaluation of project costs and unit profits, an optimal strategy can be formulated to ensure the quantitative scale of construction enterprises in the construction industry. This behavior can indirectly improve the intention of construction units to use green building materials and further promote the utilization rate of green building materials.

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Survey of evolutionary computation methods in social agent-based modeling studies

Cited by 6 publications

References 54 publications

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

When social computing meets soft computing: opportunities and insights

Research on the Influencing Factors for the Use of Green Building Materials through the Number Growth of Construction Enterprises Based on Agent-Based Modeling

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