An adaptive parameter tuning of particle swarm optimization algorithm

Xu, Gang

doi:10.1016/j.amc.2012.10.067

Cited by 107 publications

(59 citation statements)

References 27 publications

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“…For searching the optimal PID controller gains, the values assigned to the related parameters of SPSO-PID and IPSO-PID tuning methods are listed in Table 2. Cognitive parameter c 1 , social parameter c 2 , inertia weight range [ω min , ω max ] and nonlinear adjustment index k are set as empirical values according to literatures [53,54]. And values of the other parameters are obtained by calculations and tests.…”

Section: Simulation Resultsmentioning

confidence: 99%

Position control of nonlinear hydraulic system using an improved PSO based PID controller

Yin

Gong

et al. 2017

Mechanical Systems and Signal Processing

150

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Section: Simulation Resultsmentioning

confidence: 99%

Position control of nonlinear hydraulic system using an improved PSO based PID controller

Yin

Gong

et al. 2017

Mechanical Systems and Signal Processing

150

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“…There are also a number of adaptive PSO variants that are both time-varying adaptation and feedback control adaptation, including Clerc and Kennedy (2002), Banks et al (2007) and Banks et al (2008). It is worth noting that we selected the work of Xu (2013) as a competitive method in this study, as it has been showed that the work of Xu (2013) is superior performance.…”

Section: Related Workmentioning

confidence: 97%

“…Lastly, the study insists that if the speed of each iteration from the initial to the final points of the search process is equivalent to an invalidation of the cognitive parameter and the social parameter, the performance of PSO is reduced significantly. In order to overcome these problems, Xu (2013) presented an ideal velocity which decreases nonlinearly as the search process proceeds. This ideal velocity acts as a guideline for the actual velocity.…”

Section: Related Workmentioning

confidence: 99%

“…The study of Tripathi et al (2007) showed that by setting φ 1ini = φ 2fin = 2.5 and φ 1fin = φ 2ini = 0.5, this approach yielded satisfactory results on test functions on both unimodal and multimodal functions. Xu (2013) proposed a different idea of an adaptive parameter method. Based on the analysis of Jaing et al (2007), three weaknesses of PSO were addressed.…”

Section: Related Workmentioning

confidence: 99%

“…These algorithms were classic-PSO , LDIWA (Shi and Eberhart 1998), MPSO (Gang et al 2012), DAPSO , and APSO-VI (Xu 2013). The parame- Initialization (a) Set t = 0 (iteration counter) (b) Specify the parameter N (number of particles in a sub-swarm) and S (number of sub-swarm) (c) Randomly initialize the position of the N particles x 11 , x 12 , ..., x S N with x si ∈ S ⊂ R N (d) Randomly initialize the velocities of the N particles v 11 , v 12 , ..., v S N with u si ∈ S ⊂ R N (e) For i = 1, 2, ..., N do pbest si = x si (f) Set gbest = arg min s∈1,2,...,S;i∈1,2,...,N …”

Section: Parameter Settingsmentioning

confidence: 99%

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Novel self-adaptive particle swarm optimization methods

2015

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This paper proposes adaptive versions of the particle swarm optimization algorithm (PSO). These new algorithms present self-adaptive inertia weight and timevarying adaptive swarm topology techniques. The objective of these new approaches is to avoid premature convergence by executing the exploration and exploitation stages simultaneously. Although proposed PSOs are fundamentally based on commonly utilized swarm behaviors of swarming creatures, the novelty is that the whole swarm may divide into many sub-swarms in order to find a good source of food or to flee from predators. This behavior allows the particles to disperse through the search space (diversification) and the sub-swarm, where the worst performance dies out while that with the best performance grows by producing offspring. The tendency of an individual particle to avoid collision with other particles by means of simple neighborhood rules is retained in these algorithms. Numerical experiments show that the new approaches, survival sub-swarms adaptive PSO (SSS-APSO) and survival sub-swarms adaptive PSO with velocity-line bouncing (SSS-APSO-vb), outperform other competitive algorithms by providing the best solutions on Communicated by V. Loia. B Choosak Pornsing a suite of standard test problem with a much higher consistency than the algorithms compared.

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Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self‐adaptive particle swarm optimization and a clustering method

Huang

Liu

et al. 2013

J of Applied Polymer Sci

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A novel model based on a radial basis function neural network (RBF NN), chaos theory, self-adaptive particle swarm optimization (PSO), and a clustering method is proposed to predict the gas solubility in polymers; this model is hereafter called CSPSO-C RBF NN. To develop the CSPSO-C RBF NN, the conventional PSO was modified with chaos theory and a selfadaptive inertia weight factor to overcome its premature convergence problem. The classical k-means clustering method was used to tune the hidden centers and radial basis function spreads, and the modified PSO algorithm was used to optimize the RBF NN connection weights. Then, the CSPSO-C RBF NN was used to investigate the solubility of N 2 in polystyrene (PS) and CO 2 in PS, polypropylene, poly(butylene succinate), and poly(butylene succinate-co-adipate). The results obtained in this study indicate that the CSPSO-C RBF NN was an effective method for predicting the gas solubility in polymers. In addition, compared with conventional RBF NN and PSO neural network, the CSPSO-C RBF NN showed better performance. The values of the average relative deviation, squared correlation coefficient, and standard deviation were 0.1282, 0.9970, and 0.0115, respectively. The statistical data demonstrated that the CSPSO-C RBF NN had excellent prediction capabilities with a high accuracy and a good correlation between the predicted values and the experimental data.

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An adaptive parameter tuning of particle swarm optimization algorithm

Cited by 107 publications

References 27 publications

Position control of nonlinear hydraulic system using an improved PSO based PID controller

Position control of nonlinear hydraulic system using an improved PSO based PID controller

Novel self-adaptive particle swarm optimization methods

Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self‐adaptive particle swarm optimization and a clustering method

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