Enhanced particle swarm optimizer incorporating a weighted particle

Li, Nai-Jen; Wang, Wen-June; Hsu, Chen-Chien; Wei, Chang; Chou, Hao-Gong; Chang, Jun-Wei

doi:10.1016/j.neucom.2013.07.005

Cited by 36 publications

(25 citation statements)

References 31 publications

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“…The hybridized technique named PSO-GA algorithm was presented by taking the advantages of both PSO and GA algorithm for solving the nonlinear design optimization problems (Garg, 2016;Nik, Nejad, & Zakeri, 2016). The weighted particle for incorporation into the particle swarm optimization, and the enhanced particle swarm optimizer incorporating a weighted particle (EPSOWP) was developed to improve the evolutionary performance for a set of benchmark function (Li, Wang, Hsu, & Chen, 2014). Based on the random linear combination between the local best position and the global best position, the particle swarm without velocity equation (PSWV) algorithm was studied by Tungadio, Jordaan, and Siti (2016).…”

Section: Introductionmentioning

confidence: 99%

Computational fluid dynamics-based hull form optimization using approximation method

Zhang

Tezdogan

et al. 2017

Engineering Applications of Computational Fluid Mechanics

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With the rapid development of the computational technology, computational fluid dynamics (CFD) tools have been widely used to evaluate the ship hydrodynamic performances in the hull forms optimization. However, it is very time consuming since a great number of the CFD simulations need to be performed for one single optimization. It is of great importance to find a high-effective method to replace the calculation of the CFD tools. In this study, a CFD-based hull form optimization loop has been developed by integrating an approximate method to optimize hull form for reducing the total resistance in calm water. In order to improve the optimization accuracy of particle swarm optimization (PSO) algorithm, an improved PSO (IPSO) algorithm is presented where the inertia weight coefficient and search method are designed based on random inertia weight and convergence evaluation, respectively. To improve the prediction accuracy of total resistance, a data prediction method based on IPSO-Elman neural network (NN) is proposed. Herein, IPSO algorithm is used to train the weight coefficients and self-feedback gain coefficient of ElmanNN. In order to build IPSO-ElmanNN model, optimal Latin hypercube design (Opt LHD) is used to design the sampling hull forms, and the total resistance (objective function) of these hull forms are calculated by Reynolds averaged Navier-Stokes (RANS) method. For the purpose of this article, this optimization framework has been employed to optimize two ships, namely, the DTMB5512 and WIGLEY III, and these hull forms are changed by arbitrary shape deformation (ASD) technique. The results show that the optimization framework developed in this study can be used to optimize hull forms with significantly reduced computational effort. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Computational fluid dynamics-based hull form optimization using approximation method

Zhang

Tezdogan

et al. 2017

Engineering Applications of Computational Fluid Mechanics

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“…Decreasing the inertia over time introduces a shift from the exploratory (global search) to the exploitative (local search) mode [50,51]. Generally, the inertia weight ω is reduced linearly.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

2015

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In Content-Centric Networks (CCNs) as a possible future Internet, new kinds of attacks and security challenges -from Denial of Service (DoS) to privacy attacks-will arise. An efficient and effective security mechanism is required to secure content and defense against unknown and new forms of attacks and anomalies. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method to classify data into different categories. However, it suffers from the local convergence and sensitivity to selection of the cluster centroids. In this paper, we present a novel fuzzy anomaly detection system that works in two phases. In the first phase -the training phase-we propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as well-separated clusters and local optimization to determine the optimal number of clusters. When the optimal placement of clusters centroids and objects are defined, it starts the second phase. In this phase -the detection phase-we employ a fuzzy approach by the combination of two distance-based methods as classification and outlier to detect anomalies in new monitoring data. Experimental results demonstrate that the proposed algorithm can achieve to the optimal number of clusters, well-separated clusters, as well as increase the high detection rate and decrease the false positive rate at the same time when compared to some other well-known clustering algorithms.

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“…Since the state estimation is the back bone of all analyses carried out in the power system from the control centres, PSO can be an appropriate method for the target problem. Nowadays different versions of PSO algorithms exist in the literature [39][40][41][42][43][44][45][46][47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

“…This research proposes the comparison of the original PSO, ABSO [64] and GA [14] with the modified models presented in [46,51], when solving PSSE problem. The proposed method considers a set of available measurements in the control centre and estimates the missing measurements.…”

Section: Introductionmentioning

confidence: 99%

Power system state estimation solution using modified models of PSO algorithm: Comparative study

2016

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Enhanced particle swarm optimizer incorporating a weighted particle

Cited by 36 publications

References 31 publications

Computational fluid dynamics-based hull form optimization using approximation method

Computational fluid dynamics-based hull form optimization using approximation method

A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks

Power system state estimation solution using modified models of PSO algorithm: Comparative study

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