Particle Swarm Optimization

Zeugmann, Thomas; Poupart, Pascal; Kennedy, James L.; Jin, Xin; Han, Jiawei; Saitta, Lorenza; Sebag, Michèle; Peters, Jan; Bagnell, J. Andrew; Daelemans, Walter; Ting, Kai Ming; Webb, Geoffrey I.; Shirabad, Jelber Sayyad; Hüllermeier, Eyke; Matwin, Stan; Sakakibara, Yasubumi; Flener, Pierre; Schmid, Ute; Procopiuc, Cecilia M.; Lachiche, Nicolas; Fürnkranz, Johannes

doi:10.1007/978-0-387-30164-8_630

Cited by 976 publications

(94 citation statements)

References 11 publications

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“…However, the path planning performance is still weak due to the limitations imposed by the potential field model used in the study. PSO also started to be used for achieving collision-free robot navigation ( Kennedy, 2010;Nasrollahy, 2009 ). However, the results are computed slowly and are highly dependent on the shape of the obstacles.…”

Section: Path Planning With Artificial Intelligence Techniquesmentioning

confidence: 99%

Neural networks based reinforcement learning for mobile robots obstacle avoidance

Duguleană

Mogan

2016

Expert Systems with Applications

159

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Section: Path Planning With Artificial Intelligence Techniquesmentioning

confidence: 99%

Neural networks based reinforcement learning for mobile robots obstacle avoidance

Duguleană

Mogan

2016

Expert Systems with Applications

159

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“…In order to handle the above mentioned problems, we have decided to implement the particle swarm optimization algorithm of Kennedy [14]. This is an iterative method based on the interaction of multiple agents or "particles".…”

Section: Particle-swarm Optimizationmentioning

confidence: 99%

Estimation of Edge Infection Probabilities in the Inverse Infection Problem

Bóta

Krész

Pluhár

2015

Recent Advances in Computational Optimization

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Several methods have been proposed recently to estimate the edge infection probabilities in infection or diffusion models. In this paper we will use the framework of the Generalized Cascade Model to define the Inverse Infection Problem-the problem of calculating these probabilities. We are going to show that the problem can be reduced to an optimization task and we will give a particle swarm based method as a solution. We will show, that direct estimation of the separate edge infection values is possible, although only on small graphs with a few thousand edges. To reduce the dimensionality of the task, the edge infection values can be considered as functions of known attributes on the vertices or edges of the graph, this way only the unknown coefficients of these functions have to be estimated. We are going to evaluate our method on artificially created infection scenarios. Our main points of interest are the accuracy and stability of the estimation.

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“…PSO is an artificial intelligence technique inspired from the social behavior of birds, and it is used to approximate the solutions for optimization problems involving a large search space [50]. An individual amongst the bird population is known as a particle, and it represents the possible solution (a bird in the flock) for a given problem.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

Aslam

Hasan

Shahid

et al. 2016

Sensors

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The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT’ network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.

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Particle Swarm Optimization

Cited by 976 publications

References 11 publications

Neural networks based reinforcement learning for mobile robots obstacle avoidance

Neural networks based reinforcement learning for mobile robots obstacle avoidance

Estimation of Edge Infection Probabilities in the Inverse Infection Problem

Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques

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