A decomposition-based many-objective ant colony optimization algorithm with adaptive reference points

Over the past couple of decades, the research area of network community detection has seen substantial growth in popularity, leading to a wide range of researches in the literature. Nature-inspired optimization algorithms (NIAs) have given a significant contribution to solving the community detection problem by transcending the limitations of other techniques. However, due to the importance of the topic and its prominence in many applications, the information on it is scattered in various journals, conference proceedings, and patents, and lacked a focused-literature that synthesizes them in a single document. This review aims to provide an overview of the NIAs and their role in solving community detection problems. To achieve this goal, a systematic study is performed on NIAs,
followed by historical and statistical analysis of the researches involved. This would lead to the identification of future trends, as well as the discovery of related research challenges. This review provides a guide for researchers to identify new areas of research, as well as directing their future interest towards developing more effective frameworks in the context of nature-inspired community detection algorithms.

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Nature-inspired optimization algorithms for community detection in complex networks: a review and future trends

The ant colony optimization algorithm (ACO) had an exceptional performance in solving discrete optimization problems because of its design in solution construction and search strategy. However, the study of ACO in discrete many-objective optimization remains insufficient. This paper proposes a decomposition-based ACO for discrete many-objective optimization. The proposed algorithm utilizes a reinforcement learning-based adaptive pheromone updating strategy that enhances the solution construction phase’s searching ability in the high-dimensional objective space. Furthermore, an adaptive selection strategy is adopted to improve its convergence performance using different reference points. And A comparative experimental study is conducted on many-objective benchmark test cases. The experimental results indicate that the proposed algorithm achieves competitive performance on optimization quality.

A decomposition-based many-objective ant colony optimization algorithm with adaptive solution construction and selection approaches

Ant colony system has a good performance in solving Traveling Salesman Problem (TSP), but it tends to fall into local optimum and is deficient in convergence speed To address this problem, a dynamic density clustering ant colony algorithm with a filtering recommendation backtracking mechanism is proposed (DBACS) Firstly, a dynamic density clustering strategy is proposed to accelerate the convergence speed of the algorithm and improve the quality of the solution Under this strategy, the search radius is expanded dynamically to merge adjacent classes, so as to form the differential pheromone distribution The splicing paths between each class are adjusted through the ant colony algorithm to achieve better performance Secondly, a recommendation backtracking mechanism based on collaborative filtering is proposed to increase the diversity of the population, thus helping the algorithm jump out of the local optimum With the help of the collaborative filtering algorithm, some dense data points are recommended for pheromone dynamic backtracking, which can not only help algorithm jump out of the local optimum, but also help the algorithm accelerate convergence Simulation results show that the improved algorithm can obtain a better solution and higher stability Especially in solving large-scale TSP, the accuracy of the solution is significantly improved

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Dynamic Density Clustering Ant Colony Algorithm With Filtering Recommendation Backtracking Mechanism

The Mobile ad-hoc networks are auto configured systems where hubs can travel towards any direction. It doesn’t rely on any other frame to build the framework. Because of its versatility, the black hole is a real security issue to be settled. It occurs once a malignant hub referred to as black hole goes into the framework. Black hole center point exhibits its fake lead among the methodology after all disclosure. Currently, varied strategies are made arrangements for Mobile ad-hoc networks. Regardless, at interims seeing Black hole or fake hubs, the entire Mobile ad-hoc frameworks are exposed to various sorts of assaults or network attacks. Among these issues, a black hole center points plugs itself of getting a most nearest targeted hub, whose data packet must be constrained to drop. In this flooding procedures, if the communication from the genuine hub accomplishes later than the fake hub reply as requested by the standard hub. A false communication path is framed through a fake center hub. A perfect method is one in which the package reach to target with less delay and lesser overhead. In this paper we'll talk about the implementation of Ant Colony Optimization Technique and Repetitive Route Configuration with Reactive Routing Protocol for obstruction of black hole Attack in Mobile ad-hoc networks.

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Black Hole Attack Prevention in Mobile Ad-hoc Network (MANET) using Ant Colony Optimization Technique

Community detection aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. The problem can be modeled as an NP hard combinatorial optimization problem, to which multi-objective optimization has been applied, addressing the common resolution limitation problem in modularity-based optimization. In the literature, ant colony optimization (ACO) algorithm, however, has been only applied to community detection with single objective. This is due to the main difficulties in defining and updating the pheromone matrices, constructing the transition probability model, and tuning the parameters. To address these issues, a multi-objective ACO algorithm based on decomposition (MOACO/D-Net) is proposed in this paper, minimizing negative ratio association and ratio cut simultaneously in community detection. MOACO/D-Net decomposes the community detection multi-objective optimization problem into several subproblems, and each one corresponds to one ant in the ant colony. Furthermore, the ant colony is partitioned into groups, and ants in the same group share a common pheromone matrix with information learned from high-quality solutions. The pheromone matrix of each group is updated based on updated nondominated solutions in this group. New solutions are constructed by the ants in each group using a proposed transition probability model, and each of them is then improved by an improvement operator based on the definition of strong community. After improvement, all the solutions are compared with the solutions in the external archive and the nondominated ones are added to the external archive. Finally each ant updates its current solution based on a better neighbor, which may belong to an adjacent group. The resulting final external archive consists of nondominated solutions, and each one corresponds to a different partition of the network. Systematic experiments on LFR benchmark networks and eight real-world networks demonstrate the effectiveness and robustness of the proposed algorithm. The ranges of proper values for each parameter are also analyzed, addressing the key issue of parameter tuning in ACO algorithms based on a large number of tests conducted.

Jian Zhang

Papers

Multi-objective ant colony optimization algorithm based on decomposition for community detection in complex networks