نتایج جستجو برای: θ pso

تعداد نتایج: 24539  

2014
A. Erdeljan D. Capko S. Vukmirovic D. Bojanic

This paper presents a method for data model partitioning of power distribution network. Modern Distribution Management Systems which utilize multiprocessor systems for efficient processing of large data model are considered. The data model partitioning is carried out for parallelization of analytical power calculations. The proposed algorithms (Particle Swarm Optimization (PSO) and distributed ...

Journal: :Annals of Probability 2021

We show that as T→∞, for all t∈[T,2T] outside of a set measure o(T), ∫−logθTlogθT|ζ(1 2+it+ih)|βdh=(logT)fθ(β)+o(1), some explicit exponent fθ(β), where θ>−1 and β>0. This proves an extended version conjecture Fyodorov Keating (Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 372 (2014) 20120503, 32). In particular, it shows that, θ>−1, the moments exhibit phase transition at critical ...

Journal: :Journal of mechanics of continua and mathematical sciences 2023

In this paper, the author studied skeletal congruences θ^* of a distributive nearlattice S, where * represents pseudocomplement. Then described θ(I)^*, θ(I) is smallest congruence S containing n-ideal I as class and showed that I^+ n-kernel θ(I)^*. established following fundamental results: When n an upper element has shown n-kernels are precisely those n-ideals which intersection relative anni...

2003
Dieter Cremer

For the analysis of the paramagnetic spin orbit (PSO) term of the NMR spin–spin coupling constant (SSCC), ring current density and PSO density distribution are derived and used to explain magnitude and sign of the isotropic PSO term. Decomposition of the PSO components into orbital contributions helps to identify those orbital pairs (occupied, virtual) dominating the PSO term. The induction of ...

2007
LUPING FANG PAN CHEN SHIHUA LIU

-Aiming at the shortcoming of basic PSO algorithm, that is, easily trapping into local minimum, we propose an advanced PSO algorithm with SA and apply this new algorithm for solving TSP problem. The core of algorithm is based on the PSO algorithm. SA method is used to slow down the degeneration of the PSO swarm and increase the swarm’s diversity. The comparative experiments were made between PS...

2011
Xiaodong Li X. Li

Niching as an important technique for multimodal optimization has been used widely in the Evolutionary Computation research community. This chapter aims to provide a survey of some recent efforts in developing stateof-the-art PSO niching algorithms. The chapter first discusses some common issues and difficulties faced when using niching methods, then describe several existing PSO niching algori...

2006
Laura Diosan Mihai Oltean

A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-designed PSO algorithm by using ten artifi...

Journal: :JNW 2010
Jun Tang Xiaojuan Zhao

Particle swarm optimization (PSO) has shown its good search ability in many optimization problems. However, PSO often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO, namely LSPSO, to solve this problem by employi...

Journal: :IJSIR 2013
George M. Cavalcanti-Júnior Fernando Buarque de Lima Neto Carmelo J. A. Bastos Filho

Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, in some domains even well-established techniques such as Particle Swarm Optimization (PSO) may not present the necessary ability to generate diversity during the process of the swarm convergence. Indeed, this is the major difficulty to use PSO to tackle dynamic problems. Many efforts to overcome...

Journal: :JCP 2017
Monir Foqaha Mohammed Awad

Function approximation is an important type of supervised machine learning techniques, which aims to create a model for an unknown function to find a relationship between input and output data. The aim of the proposed approach is to develop and evaluate a function approximation models using Radial Basis Function Neural Networks (RBFN) and Particles Swarm Optimization (PSO) algorithm. We propose...

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