Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks

Shen, Meie; Zhan, Zhi-Hui; Chen, Wei-Neng; Gong, Yue-Jiao; Zhang, Mengjie; Li, Yun

doi:10.1109/tie.2014.2314075

Cited by 112 publications

(39 citation statements)

References 35 publications

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“…The most popular applications in machinery are trajectory optimization [30], defect classification [31,32], and scheduling problems [33,34]. Applications in communication consist of routing optimization [35], wireless communication system optimization [36], filter design optimization [37], etc. Relatively, there are very limited study or applications in biology [38], artificial intelligence [39,40], and some other crossing fields [41][42][43].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Cai

Zhang

2018

J Wireless Com Network

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This paper presents an ensemble learning particle swarm optimization (ELPSO) algorithm for real-time indoor localization based on ultra-wideband (UWB). Indoor localization problem can be formulated as an optimization problem to predict the target. The proposed algorithm expands the original PSO into ELPSO under superbest guide, which is a parameter employed to identify the top gbest by learning from three individual algorithms and updated asynchronously. The performance of the proposed ELPSO is evaluated by using the CEC2005 benchmark and compared with each individual algorithm and other state-of-the-art optimization algorithms. The feasibility of the proposed ELPSO is demonstrated in both 2D and 3D UWB indoor localization system generating promising results.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Cai

Zhang

2018

J Wireless Com Network

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“…Every component searches the problem space and advances to the solution using the best personal space as well as the best place achieved by population. The main advantages of Flock Algorithm could be cited as follows: simple concept, easy implementation, high power to control the parameters, high search power, high computation efficiency compared to mathematical algorithms and creative techniques [11] [12]. Many researchers discussed the application of Particle Swarm Optimization Algorithm (PSO) to solve the problem of routing with QoS limitations.…”

Section: Related Workmentioning

confidence: 99%

Multicast Routing Problem Using Tree-Based Cuckoo Optimization Algorithm

Sardarpour¹,

Hosseinzadeh²,

Effatparvar³

2016

ijacsa

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Abstract-The problem of QoS multicast routing is to find a multicast tree with the least expense/cost which would meet the limitations such as band width, delay and loss rate. This is a NPComplete problem. To solve the problem of multicast routing, the entire routes from the source node to every destination node are often recognized. Then the routes are integrated and changed into a single multicast tree. But they are slow and complicated methods. The present paper introduces a new tree-based optimization method to overcome such weaknesses. The recommended method directly optimizes the multicast tree. Therefore a tree-based typology including several spanning trees is created which combines the trees two by two. For this purpose, the Cuckoo Algorithm is used which is proved to be well converged and makes quick calculations. The simulation conducted on different types of network typologies proved that it is a practical and influential algorithm.

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“…PSO is proposed by Eberhart and Kennedy in 1995, and has fast developed in recent years [38][39][40]. PSO is inspired by the foraging of birds.…”

Section: Optimize Nn By Psomentioning

confidence: 99%

When Neural Network Computation Meets Evolutionary Computation: A Survey

Chen

Zhan

Shi

et al. 2016

Advances in Neural Networks – ISNN 2016

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Abstract. Neural network (NN) and evolutionary computation (EC) are two of the most popular and important techniques in computational intelligence, which can be combined together to solve the complex real world problems. This paper represents a review of the researches that combined NN and EC. There are 3 main research focuses as follows. In the first research focus, EC algorithms have been widely used to optimize the structure and parameter of the NN, including weight, structure, learning rates, and others. In the second research focus, lots literatures have witnessed that EC based NNs are widespread in the applications such as classification, automatic control, prediction, and many other fields. These two kinds of researches into combining NN and EC are mainly focuses on using EC algorithms to optimize NN, to enhance the NN performance and the NN application ability. Our survey finds that particle swarm optimization is the most popular EC algorithm that the researchers choice to optimize NN in recent year, while genetic algorithm and differential evolution are also widely used. In the third research focus, there are also researches adopted NN as a tool to enhance the performance of EC algorithms. Although the literatures in this focus are not as many as the above two focuses, the existing results show that NN has great potential in enhancing EC algorithms. The survey shows that when NN and EC meet, combining them would result in an effective way to deal with the real world application. This interesting research topic has become more and more significant in the field of computer science and still has much room for development.

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Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks

Cited by 112 publications

References 35 publications

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Multicast Routing Problem Using Tree-Based Cuckoo Optimization Algorithm

When Neural Network Computation Meets Evolutionary Computation: A Survey

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