An adaptive hybrid algorithm for social networks to choose groups with independent members

Hadikhani, Parham; Hadikhani, Pooria

doi:10.1007/s12065-020-00384-x

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…As the future works, in order to evaluate the lifetime of sensor nodes before forming the connectivity formation, we can use the MPLM method in (Hao et al, 2018). Moreover, we would like to explore other meta-heuristic algorithms like (Hadikhani & Hadikhani, 2020) to improve this method. In the proposed method, we can have the durability or persistence of sensor nodes actively in the network.…”

Section: Discussionmentioning

confidence: 99%

Energy-Efficient Topology to Enhance the Wireless Sensor Network Lifetime Using Connectivity Control

Yari

Hadikhani²,

Asgharzadeh

2020

JTDE

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Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of research has been done to expand their applications and improve their efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two metaheuristic algorithms, namely SA (Simulated Annealing) and VNS (Variable Neighbourhood Search), to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

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Section: Discussionmentioning

confidence: 99%

Energy-Efficient Topology to Enhance the Wireless Sensor Network Lifetime Using Connectivity Control

Yari

Hadikhani²,

Asgharzadeh

2020

JTDE

Self Cite

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“…Classical clustering can be categorized as hierarchical [15], distribution-based [16], density-based [17], learning network clustering [18], and partition clustering [19]. Since clustering is an NP-hard problem, a vast majority of evolutionary clustering methods have been proposed to solve clustering due to their strength in optimization [20]. Hadikhani et al [21] proposed a hybrid particle swarm optimization with k-means.…”

Section: Related Work a Evolutionary Approachesmentioning

confidence: 99%

Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution Clustering with Manifold Learning and Incremental Technique

Hadikhani¹,

Lai²,

Ong³

et al. 2023

Preprint

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<p>Most deep clustering methods despite providing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of the search and it causes them to get stuck in the local optimum. In addition, the majority of existing deep clustering approaches perform clustering with the knowledge of the number of clusters, which is not practical in most real scenarios where such information is not available. To address these problems, this paper presents a novel automatic deep sparse clustering approach based on an evolutionary algorithm called Multi-Trial Vectorbased Differential Evolution (MTDE). Sparse auto-encoder is first applied to extract embedded features. Manifold learning is then adopted to obtain representation and extract the spatial structure of features. Afterward, MTDE clustering is performed without prior information on the number of clusters to find the optimal clustering solution. The proposed approach was evaluated on various datasets, including images and time-series. The results demonstrate that the proposed method improved MTDE by 18.94% on average and compared to the most recent deep clustering algorithms, is consistently among the top three in the majority of datasets. Source code is available on Github: https://github.com/parhamhadikhani/ADSMTDE_Clustering. </p>

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“…PSO is a population-based optimization algorithm [33]. A population is made up of a number of particles and each particle represents a solution and moves according to its speed.…”

Section: Proposed Clusteringmentioning

confidence: 99%

A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation

Hadikhani¹,

Lai²,

Ong³

2022

Preprint

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Human activity discovery aims to distinguish the activities performed by humans, without any prior information of what defines each activity. Most methods presented in human activity recognition are supervised, where there are labeled inputs to train the system. In reality, it is difficult to label data because of its huge volume and the variety of activities performed by humans. In this paper, a novel unsupervised approach is proposed to perform human activity discovery in 3D skeleton sequences. First, important frames are selected based on kinetic energy. Next, the displacement of joints, set of statistical, angles, and orientation features are extracted to represent the activities information. Since not all extracted features have useful information, the dimension of features is reduced using PCA. Most human activity discovery proposed are not fully unsupervised. They use pre-segmented videos before categorizing activities. To deal with this, we used the fragmented sliding time window method to segment the time series of activities with some overlapping. Then, activities are discovered by a novel hybrid particle swarm optimization with a Gaussian mutation algorithm to avoid getting stuck in the local optimum. Finally, k-means is applied to the outcome centroids to overcome the slow rate of PSO. Experiments on three datasets have been presented and the results show the proposed method has superior perfor- * Corresponding author.

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An adaptive hybrid algorithm for social networks to choose groups with independent members

Cited by 14 publications

References 23 publications

Energy-Efficient Topology to Enhance the Wireless Sensor Network Lifetime Using Connectivity Control

Energy-Efficient Topology to Enhance the Wireless Sensor Network Lifetime Using Connectivity Control

Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution Clustering with Manifold Learning and Incremental Technique

A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation

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