Community detection in social networks with node attributes based on multi-objective biogeography based optimization

Reihanian, Ali; Feizi‐Derakhshi, Mohammad‐Reza; Aghdasi, Hadi S.

doi:10.1016/j.engappai.2017.03.007

Cited by 42 publications

(26 citation statements)

References 47 publications

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“…Similar to other evolutionary algorithms (e.g., PSO, GA, DE, ACO, etc.) the GWO and BBO are population-based search techniques which their advantages over the mentioned optimization techniques are shown in some studies (Reihanian, et al [69] and references therein). Bui [32] employed the GWO and BBO as well as gravitational search algorithm (GSA) for optimizing the performance of the MLP for investigating forest fire susceptibility in a fire-prone area of Vietnam.…”

Section: Discussionmentioning

confidence: 99%

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Moayedi

Osouli

Bui

et al. 2019

Sensors

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Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models.

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Section: Discussionmentioning

confidence: 99%

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Moayedi

Osouli

Bui

et al. 2019

Sensors

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“…The simulated traffic is constant bit rate (CBR). In order to evaluate the performance analysis of the proposed MCD method is compared with the existing multi-objective discrete biogeography based optimization (BBO) [21], modularity, SimAtt maximization BBO Algorithm [22] in terms of SA  . The metric used to compute the best compromise solution among set of non-dominated ones considers two aspects of node: attributes and link weights.…”

Section: Simulation Results and Observationmentioning

confidence: 99%

“…Reihanian et al, [21] has proposed a multi-objective discrete biogeography based optimization (BBO) algorithm to compute the communities. This algorithm uses the Pareto-based approach for community detection using a new metric, SimAtt along with Modularity for detecting communities [22].…”

Section: Problem Methodologymentioning

confidence: 99%

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Finding Communities in Social Networks with Node Attribute and Graph Structure using Jaya Optimization Algorithm

Ahuja¹,

Singh²

2018

IJFGCN

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“…An efficient multi-objective optimization algorithm using the differential evolution (DE) algorithm is proposed to solve multi-objective optimal power flow (MO-OPF) problems [37]. BBO has also been modified to solve multi-objective optimization problems (MOPs) [38][39][40][41][42][43][44][45], such as, multi-objective biogeography-based optimization based on predator-prey approach [38], indoor wireless heterogeneous networks planning [39], automated warehouse scheduling [40], and community detection in social networks with node attributes [41]. Work in the literature [42] is focused on numerical comparisons of migration models for multi-objective biogeography-based optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems

2017

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In order to improve the performance of optimization, we apply a hybridization of adaptive biogeography-based optimization (BBO) algorithm and differential evolution (DE) to multi-objective optimization problems (MOPs). A model of multi-objective evolutionary algorithms (MOEAs) is established, in which the habitat suitability index (HSI) is redefined, based on the Pareto dominance relation, and density information among the habitat individuals. Then, we design a new algorithm, in which the modification probability and mutation probability are changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. The mutation operators based on DE algorithm, are modified, and the migration operators based on number of iterations, are improved to achieve better convergence performance. Numerical experiments on different ZDT and DTLZ benchmark functions are performed, and the results demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution properties comparing to the other MOEAs, and can solve more complex multi-objective optimization problems efficiently. distribution of solutions [6]. Second, there are several good results based on the hybridization of BBO with differential evolution (DE) [8,9]. Last, blend combination operators have been widely and successfully used in other population-based optimization algorithms [10].The original contributions of this paper include the following. Firstly, the model of applying BBO for multi-objective evolutionary algorithm was established, in which the habitat suitability index is redefined, based on the Pareto dominance relation and density information among the habitat individuals. Motivated by the work in [11,12], we hybridize adaptive BBO with DE to design a new algorithm for multi-objective optimization problems. In this algorithm, the mutation operator based on DE algorithm was modified, and the migration operator based on the number of iterations was re-designed to achieve better performance. Furthermore, the modification probability and mutation probability are adaptively changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. Experiments performed on ZDT and DTLZ benchmark functions show that the obtained Pareto solution set can approximate to the Pareto optimal front, and has good diversity and uniform distribution. The results also demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution.The paper is organized as follows. Section 2 describes the related work about BBO algorithm. The basic theories of multi-objective optimization are presented in Section 3.The MABBO algorithm procedure for multi-objective optimization problems are proposed in Section 4. Section 5 shows the simulation and experiment results. ...

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Community detection in social networks with node attributes based on multi-objective biogeography based optimization

Cited by 42 publications

References 47 publications

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Finding Communities in Social Networks with Node Attribute and Graph Structure using Jaya Optimization Algorithm

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems

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