Improved strategy of particle swarm optimisation algorithm for reactive power optimisation

Lu, Jingui; Zhang, Li; Yang, Huanxing; Du, Jing

doi:10.1504/ijbic.2010.030041

Cited by 36 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we focus on swarm intelligence represented by the ant colony system and an artificial neural network simulating the brain's working principles and analyse the similarities between them by the approaches of complexity study, in order to reveal the inherent relationship among the different bio-inspired computation methods. The same analysis can also be obtained with other swarm intelligence, especially particle swarm optimization (Abraham et al, 2010;Deng et al, 2010;Jain et al, 2010;Lu et al, 2010;Singh et al, 2010). Firstly, the similarities between swarm intelligence and neural network have been analysed qualitatively from system structure and operation mechanisms.…”

Section: Discussionmentioning

confidence: 75%

An analytical approach to the similarities between swarm intelligence and artificial neural network

Xiao

Tao

Chen

2011

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, the authors focus on swarm intelligence represented by the ant colony system and artificial neural network simulating the brain working principles, and analyse the similarities between them by the approaches of a complexity study. Firstly, the similarities between swarm intelligence and neural network have been analysed qualitatively from system structure and operation mechanisms and the results show that there exist obvious similarities in overall disciplines between them. Moreover, the mapping relationships between swarm intelligence and artificial neural network at algorithm level have been revealed using the travelling salesman problem (TSP), which illustrates that these two algorithms are also similar from the viewpoint of numeric computing and simulating when they are directly used to solve a complex problem. In addition, the swarm intelligence algorithm and the artificial neural network algorithm are adopted to solve an engineering practical problem, which can be transformed into the TSP. The results show that the performance of these two algorithms still has close similarities. The purpose of the above work is to construct the inherent relationship among different bio-inspired computation methods, which has both important theoretic significance and practical value to reveal the generation and operation mechanisms of human intelligence.

show abstract

Section: Discussionmentioning

confidence: 75%

An analytical approach to the similarities between swarm intelligence and artificial neural network

Xiao

Tao

Chen

2011

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Yang et al (2007) denoted that PSO is rapidly converging towards an optimum, simple to compute, easy to implement and free from the complex computation in GA. Also, Zhang et al (2006) and Zhang and Cai (2010) applied both PSO and GA on limited resource programming, and the results suggested that the performance of PSO is better than that of GA. There are still many other variants of PSO (Abraham et al, 2010;Deng et al, 2010;Jain et al, 2010;Lu et al, 2010;Singh et al, 2010); for more details please refer to the corresponding references. Since the operating efficiency of PSO is better than that of GA, the proposed solving approach TPSO, in this study, is based on PSO.…”

Section: The Concept Of Particle Swarm Optimizationmentioning

confidence: 99%

Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management

Che

Chiang

Che

2011

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Supply chain planning has been regarded as a key strategic decision-making activity for the enterprises under the current business circumstances. From the point of supply chain planning, the important issues are to find suitable and quality partners and to decide upon an appropriate productiondistribution plan. In this study, hence, we address to develop a decision methodology for supply chain planning in multi-echelon non-balanced supply chain system, taking into account such four criteria as cost, quality, delivery and supplier relationship management and considering quantity discount and capacity constraints. The proposed methodology is based on the analytic network process and turbo particle swarm optimization (TPSO), to evaluate partners and to determine an optimal supply chain network pattern and production-distribution mode. Finally, to demonstrate the performance of the proposed TPSO algorithm, comparative numerical experiments are performed by TPSO, particle swarm optimization (PSO) and genetic algorithm (GA). Empirical analysis results demonstrate that TPSO can outperform PSO and GA in non-balanced supply chain planning problems.

show abstract

“…[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] In particular, many results have been reported on the application of PSO techniques to MUD. [27][28][29][30][31] By contrast, few work has used the PSO to assist the design of MUT.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimisation Aided MIMO Multiuser Transmission Designs

Wang¹,

Chen²,

Hanzo³

2012

Jnl of Comp & Theo Nano

View full text Add to dashboard Cite

Bio-inspired computational methods have found wide-ranging applications in signal processing and other walks of engineering. The main attraction of adopting bio-inspired computational intelligence algorithms is that they may facilitate global or near global optimal designs with affordable computational costs. In this contribution, particle swarm optimisation (PSO) is invoked for designing optimal multiuser transmission (MUT) schemes for multiple-input multiple-output communication. Specifically, we consider the minimum bit-error-rate (MBER) linear MUT using PSO and we design a PSO aided MBER generalised vector precoding for nonlinear MUT. These PSO aided MUT techniques compare favourably with the state-of-the-art conventional schemes, in terms of performance and complexity.

show abstract

Improved strategy of particle swarm optimisation algorithm for reactive power optimisation

Cited by 36 publications

References 16 publications

An analytical approach to the similarities between swarm intelligence and artificial neural network

An analytical approach to the similarities between swarm intelligence and artificial neural network

Using analytic network process and turbo particle swarm optimization algorithm for non-balanced supply chain planning considering supplier relationship management

Particle Swarm Optimisation Aided MIMO Multiuser Transmission Designs

Contact Info

Product

Resources

About