Modified Particle Swarm Optimization algorithm by enhancing search ability of global optimal particle

Zhang, Wei; Shi, Yibing; Ma, Dong; Liu, Guozhen

doi:10.1109/icemi.2015.7494232

Cited by 2 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimal pilot index on each antenna, P t ; 1: (P l∈P t is fixed) Create the initial population ∈ N P s ×J , where P s is population size and J = N T P. Each row of can be expressed as the orthogonal pilot indices. Compute the fitness of each individual in by using (36) with a definite evaluation criterion; 2: Make the inertia coefficient of the global optimal particle is larger than that of other particles, and the search direction of the global optimal particle is adjusted by using an orthogonal direction search [36]. 3: Run according to the basic particle swarm optimization process until I max is reached.…”

Section: Outputmentioning

confidence: 99%

See 1 more Smart Citation

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Zhang

Gao

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this paper, we investigate the channel estimation and decoding methods exploiting the channel sparsity in pilot-assisted Multiple-Input Multiple-Output (MIMO) Vector Orthogonal Frequency Division Multiplexing (V-OFDM) systems. Based on the sparse multipath channels, we utilize orthogonal and non-orthogonal pilot schemes to design the compressed sensing (CS) measurement process. For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with the simplified evaluation criterion to improve the pilot design procedure. Meanwhile, the effect of pilot insertion on the Peak-to-Average Power Ratio (PAPR) is reduced by a particular precoding matrix method without affecting the decoding complexity. Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method has higher channel estimation accuracy with unknown sparsity. On the other hand, to overcome the complexity of CS-based decoding, we design the fully connected Deep Neural Network (FC-DNN) decoders, which combine the results of channel estimation results with the prevalent neural network technology. We observe that when the sparse channels are estimated accurately by CS methods, the proposed FC-DNN can achieve the same performance as the high-precision linear decoder by using the time-domain pilots and channel estimation results. INDEX TERMS MIMO, OFDM, compressed sensing, channel estimation, peak to average power ratio, decoding, neural networks.

show abstract

Section: Outputmentioning

confidence: 99%

“…Because of the forced random variation mechanism and orthogonalization direction processing [36], which are added in the population iteration process, the proposed PSO-based search algorithm has the best standard deviation and optimal results performance with different criteria.…”

Section: A Papr Improvement and Simulationmentioning

confidence: 99%

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Zhang

Gao

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

Optimal Allocation of Distributed Thyristor Controlled Series Compensators in Power System Considering Overload, Voltage, and Losses with Reliability Effect

Khalili

Aliasghari

Najmi³

et al. 2022

Energies

View full text Add to dashboard Cite

In this paper, optimal allocation of a distributed thyristor-controlled series compensator (DTCSC) in a power system is presented to minimize overload, voltage deviations, and power losses while improving system reliability. The decision variable was defined as the optimal reactance of the DTCSC in the power system, which was determined using a new meta-heuristic algorithm named the improved equilibrium optimization algorithm (IEOA). A nonlinear inertia weight reduction strategy was used to improve the performance of traditional EOA in preventing premature convergence and facilitate a quick global optimum solution. The effect of system critical line outage was evaluated for each of the considered goals. To evaluate the effectiveness of the proposed methodology, IEOA capability was compared with particle swarm optimization (PSO) and manta ray foraging optimizer (MRFO) methods. Simulations were carried out considering different scenarios on 14- and 118-bus test systems. The results showed that, for all scenarios, optimal allocation of DTCSC could result in a significant reduction in overloading, voltage deviation of network buses, as well as power losses under the condition of line outage, due to the optimal injection of reactive power. In all investigated scenarios, our results attested to the superiority of the IEOA over the traditional EOA, PSO, and MRFO in achieving a better value for the objective function. In addition, the results showed that improving reliability in the objective function could eliminate overloading, and hence, introduce further improvement in each of the objectives.

show abstract

Modified Particle Swarm Optimization algorithm by enhancing search ability of global optimal particle

Cited by 2 publications

References 9 publications

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Optimal Allocation of Distributed Thyristor Controlled Series Compensators in Power System Considering Overload, Voltage, and Losses with Reliability Effect

Contact Info

Product

Resources

About