Search Results

The work presented in this paper falls within the general framework of wireless sensor networks. We focus on the study and implementation of new distributed algorithms that use metaheuristics biologically inspired to solve the localization problem ...

In order to achieve the goal of dynamically adjusting daily passenger flow to effectively control the overall efficiency of the transportation system, this study constructs a real-time monitoring and prediction system for subway passenger flow based on ...

Position is one of the basic attributes of an object, which is one of the key technologies for its collaborative operation. As a distributed sensing method, wireless sensor networks (WSNs) have become a feasible solution especially in satellite signal ...

In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to ...

The flexible load with the function of ' virtual energy storage ' can change the original load distribution in time and space and reduce the peak-valley difference. However, the existing planning has not carried out in-depth research on it. Firstly, by ...

Efficiently representing and generating combinations can allow the seamless visualization, sampling, and evaluation of combinatorial architectures. In this paper, being relevant to tackle resource allocation problems ubiquitously, we address the subset ...

Gradient-free stochastic optimization algorithms are well-known for finding suitable parameter configurations over independent runs ubiquitously. Attaining low variability of convergence performance through independent runs is crucial to allow further ...

For the congestion phenomena of networks, it has been provided with a new prediction method for service flow (based on Particle Swarm Optimisation and Wavelet Neural Network Prediction PSOWNNP). Firstly, this method is using the wavelet exchange to ...

The MCA tuning problem consists in finding the best values for the parameters/coefficients of Motion Cueing Algorithms (MCA). MCA are used to control the movements of robotic motion platforms employed to generate inertial cues in vehicle simulators. ...

Multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to ...

Derivative-free optimization is an area of long history which has so many applications in different fields. It has lately received considerable attention within the engineering community. This paper describes a random derivative-free algorithm for ...

Particle swarm optimization (PSO) algorithms have been widely used to solve a variety of optimization problems. Their success has motivated researchers to extend the use of these techniques to the multi-objective optimization field. However, most of ...

The performance of particle swarm optimization (PSO) greatly depends upon the effective selection of vital tuning metric known as acceleration coefficients (especially when applied to design space exploration (DSE) problem) which incorporates ability to ...

Particle swarm optimization (PSO) is a robust and popular stochastic population-based global optimization method that simulates social behavior among independent agents (particles). PSO is increasingly used to solve difficult high-dimensional and ...

This paper proposes a fuzzy version of the crisp cPSC (Constructive Particle Swarm Clustering), called FcPSC (Fuzzy Constructive Particle Swarm Clustering). In addition to detecting fuzzy clusters, the proposed algorithm dynamically determines a ...

We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for GA-PSO-FMS), in which a GA is ...

Using the global search ability and optimize the network structure and connection power of artificial neural network at the same time by particle swarm optimization algorithm and a new training of BP neural network was going on, then a nonlinear ...

The web offers a huge amount of information from which ontologies can be developed. The available information on the web can also be harnessed to help create correct ontologies from other documents. Taxonomy or hierarchy of concepts is part of ontology ...

Based on the analysis of the characteristics of combinatorial testing, an organizational evolutionary particle swarm algorithm (OEPST) to generate test cases for combinatorial testing is proposed. This algorithm is used to select the test cases of local ...

This paper describes the usage of peak functions in the heat load modeling of district heating system. Heat load is approximated by the sum of time dependent and temperature dependent components. The temperature dependent component is approximated using ...