Community Detection in Modular Complex Networks Using an Improved Particle Swarm Optimization Algorithm

Abstract

Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a local improvement operator to effectively discover community structure in the modular complex networks when employing the modularity density metric as a single-objective function. The framework of the proposed algorithm consists of three main steps: an initialization strategy, a movement strategy based on perturbation genetic operators, and an improvement operator. The key idea behind the improvement operator is to determine and reassign the complex network nodes that are located in the wrong communities if the majority of their topological links do not belong to their current communities, making it appear that these nodes belong to another community. The performance of the proposed algorithm has been tested and evaluated when applied to publicly-available modular complex networks generated using a flexible and simple benchmark generator. The experimental results showed the effectiveness of the suggested method in discovering community structure over modular networks of different complexities and sizes.

Optimization Algorithms for Enhanced Parameter Estimation in Mathematical Modelling

“…In this section, we present the results obtained from the systematic evaluation of optimization algorithms for parameter estimation in Models ( 1) and (2). Our analysis includes a comparison of algorithm performance in terms of Average Error, Max Iterations, and average speed.…”

“…Given two different statistical models with different differential equations, Model (1) and Model (2), and the different synthetic data generated by these models, the problem is to find optimal values for model parameters that minimize the mean the square error of the predictions Models between on and artificial data. We seek to find the parameter vector θ m that minimizes the objective function J m (θ m ):…”

“…In light of the announced development and clear leaps that we are witnessing in various fields of scientific and digital research, the main credit for all of this was establishing the correct controls in estimating system parameters [1,2]. This is considered one of the most important challenges in the field of confronting the wheel of progress and shortening distances and times [3,4].…”

“…Furthermore, in 2023, Niu et al presented a study to compare models for estimating reference evaporation and transpiration based on machine learning algorithms, where the study is specific to Zhengzhou pretreatment of lignocellulosic biomass in chemistry [6,7,8]. This work attempts to address this fundamental challenge by providing a comprehensive framework for model optimization and fit, with a particular focus on model (1) and model (2). The framework not only analyzes the performance of multiple optimization algorithms but also uses visualization techniques to gain deeper insights into the underlying parameter space by systematically testing the sensitivity, convergence and accuracy of these algorithms the aim of this review for researchers for their specific modeling needs is to provide a principled approach for choosing the best optimization strategy and in doing so, contribute to computer modeling and a broader productivity spectrum occurs, providing a useful and scalable method that can be easily applied to a variety of scientific inquiries.…”