A novel hybrid knowledge of firefly and pso swarm intelligence algorithms for efficient data clustering

Danesh, Malihe; Shirgahi, Hossein

doi:10.3233/jifs-17170

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…The global objective is normalized using min-max normalization method and is shown as equation (4). Constraint (5) ensures that one container can only be located at one location exactly. Constraint (6) guarantees that no container can stack above an empty location in the same stack.…”

Section: Model Formulationmentioning

confidence: 99%

“…Thereby, metaheuristic algorithms are applied to find the feasible solutions in a reasonable time. As a classical metaheuristic algorithm, PSO is similar to other evolutionary algorithms such as genetic algorithm (GA) [2,3], ant colony optimization algorithm (ACO) [4], firefly algorithm (FA) [5], and artificial fish swarm algorithm (AFSA) [6]. The common characteristic of above-mentioned algorithms is to simulate a certain feature of a natural creature to search randomly through the solution space.…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Ai-hu

et al. 2019

Mathematical Problems in Engineering

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Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

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Section: Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Ai-hu

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Clustering is one of the most critical issues in probabilistic graph mining with many applications in social, biological, transportation networks, and so on. Generally, the purpose of graph clustering is to discover the hidden patterns in the nodes so that the nodes in each cluster are as similar as possible (according to some metrics of similarity or difference) and are different from the nodes in other clusters (Danesh & Shirgahi, 2017; Halim et al, 2021). Due to the probability of connections in these graphs, it is difficult to cluster them effectively such that the existing clusters are as dense as possible and have high reliability.…”

Section: Introductionmentioning

confidence: 99%

A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications

Danesh

Dorrigiv

2023

Expert Systems

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Given the growth of uncertainty in the real world, analysing probabilistic graphs is crucial. Clustering is one of the most fundamental methods of mining probabilistic graphs to discover the hidden patterns in them. This survey examines an extensive and organized analysis of the clustering techniques of large probabilistic graphs proposed in the literature. First, the definition of probabilistic graphs and modelling them are introduced. Second, the clustering of such graphs and their challenges, such as uncertainty of edges, high dimensions, and the impossibility of applying certain graph clustering techniques directly, are expressed. Then, a taxonomy of clustering approaches is discussed in two main categories: threshold-based and possible worlds-based methods. The techniques presented in each category are explained and examined. Here, these methods are evaluated on real datasets, and their performance is compared with each other. Finally, the survey is summarized by describing some of the applications of probabilistic graph clustering and future research directions.clustering, possible worlds-based methods, probabilistic graph, threshold-based methods | INTRODUCTIONGraphs are powerful tools for modelling large interconnected data networks that are highly developed in the community and the surrounding natural world. Social networks, traffic networks, genome databases, and knowledge graphs are samples of common graphs in which entities are considered nodes, and the connections between them are considered the edges. However, the uncertainty in graph data is an undeniable problem created for various reasons such as noise in measurement methods (Aggarwal & Yu, 2009), vague and inaccurate information sources (Cheng et al., 2015), lack of the required accurate information (Zhu et al., 2015), inference and prediction models (Adar & R'e, 2007), and protecting private information (Boldi et al., 2012). In these cases, the data is represented as an uncertain or probabilistic graph in which uncertainty is assigned to each graph component as a probabilistic value. It means that the probabilistic graph can be due to probabilistic nodes, edges, attributes, or a combination of them (Khan et al., 2018).However, most of the existing probabilistic graphs were formed due to probabilistic edges. Each probabilistic edge indicates the possibility of a connection between two corresponding nodes. For example, in protein interactive networks, the interaction between the two proteins is inferred experimentally and is a probabilistic value. Alternatively, in social networks, complex relationships between users, such as trust or influence, are not directly available and are indirectly inferred from user activity (Li et al., 2021). Therefore, most recent studies in probabilistic graphs have focused on these types of graphs. Accordingly, each edge is labelled with the probability of the presence of that connection.Considering the increasing number of applications with uncertain data, mining probabilistic graphs as uncertain structured data wi...

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A Review on Swarm Intelligence Algorithms Applied for Data Clustering

Gowda

Lakshmikantha

2021

Advances in Intelligent Systems and Computing

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A novel hybrid knowledge of firefly and pso swarm intelligence algorithms for efficient data clustering

Cited by 10 publications

References 11 publications

Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Particle Swarm Optimization Using Neighborhood‐Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications

A Review on Swarm Intelligence Algorithms Applied for Data Clustering

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