Analyzing evolutionary optimization and community detection algorithms using regression line dominance

With the rapid upliftment of technology, there has emerged a dire need to 'fine-tune' or 'optimize' certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.

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“…Alternative techniques like visual analysis, which considers direct comparison of solutions would be useful [65,66].…”

Section: Discussionmentioning

confidence: 99%

Introductory Review of Swarm Intelligence Techniques

Thounaojam

Biswas

Malakar

et al. 2022

Studies in Computational Intelligence

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“…LianDuan et al [32] present a Correlation analysis for community structure detection by using Modularity function, Greedy and the fast unfolding search exercise. Anupam Biswas [33] present an Evolutionary algorithm based optimized community detection algorithm. The methodology relies simply on linear regression and quintile plots to explain the dominance of one algorithm over another.…”

Section: Community Detection Over Snsmentioning

confidence: 99%

Review of Community Detection over Social Media:Graph Prospective

Jain¹,

Singh²

2019

ijacsa

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Community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. Community detection algorithm is used to identify the social atoms that are more densely interconnected relatively to the rest over the social media platform. Recently researchers focused on group-based algorithm and member-based algorithm for community detection over social media. This paper presents comprehensive overview of community detection technique based on recent research and subsequently explores graphical prospective of social media mining and social theory (Balance theory, status theory, correlation theory) over community detection. Along with that this paper presents a comparative analysis of three different state of art community detection algorithm available on I-Graph package on python i.e. walk trap, edge betweenness and fast greedy over six different social media data set. That yield intersecting facts about the capabilities and deficiency of community analysis methods.

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“…In the SSO community discovery application, because the individual location and the adaptation values are different, the natural node that belongs to the community is also different. Populations are required to have more distinct diversity characteristics [3,[21][22][23][24][36][37][38][39][40][41]. In view of this, we further divide the male and female populations into two categories-elite and non-elite-which are sorted according to their fitness.…”

Section: Further Improvement By the Elites And Non-elitesmentioning

confidence: 99%

“…It is widely accepted that a community should have dense intra-connections and sparse inter-connections [1]. Most of complex networks imply a community structure [2][3][4][5]; their vertices are organized into groups, called communities, clusters, or modules. These include the functional structure of the protein networks, relationships in social networks, link relation of web pages on the Internet, cooperative modules in power network systems, and so on.…”

Section: Introductionmentioning

confidence: 99%

Community Detection Based on Differential Evolution Using Social Spider Optimization

Wang

Xue-jun

et al. 2017

Symmetry

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Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.

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Analyzing evolutionary optimization and community detection algorithms using regression line dominance

Cited by 31 publications

References 25 publications

Introductory Review of Swarm Intelligence Techniques

Introductory Review of Swarm Intelligence Techniques

Review of Community Detection over Social Media:Graph Prospective

Community Detection Based on Differential Evolution Using Social Spider Optimization

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