A PSO method with nonlinear time-varying evolution based on neural network for design of optimal harmonic filters

Chang, Ying-Pin; Ko, Chia-Nan

doi:10.1016/j.eswa.2008.08.007

Cited by 48 publications

(17 citation statements)

References 21 publications

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“…Constraints (6) guarantee that no more than one batch is produced in each period. The tardiness cost of producing the kth order of product type j in period t, namely a jkt , is computed by constraints (7). Finally, constraints (8)- (10) describe the domain of decision variables.…”

Section: Proceedings Of the Ieeementioning

confidence: 99%

“…In addition, some comprehensive overviews of batch scheduling problems arising in the process industry can be found in Kallrath [3] and Floudas et al [4]. In this paper, we try to solve the SBPSP by PSO algorithm which is originally introduced by Kennedy and Eberhart [5] and has been used across a wide range of applications [6][7][8]. Some strategies including a scale-based repair procedure and a combination of AIA algorithm [9] are developed in the proposed PSO.…”

Section: Introductionmentioning

confidence: 97%

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PSO algorithm for a single machine scheduling problem with batching in chemical industries

Yan

Tang

2009

2009 IEEE International Conference on Automation and Logistics

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In this paper, a single machine batch process scheduling problem (SBPSP) integrating batching decision is investigated. The integration problem of batching and scheduling is to allocate the demands from different customer orders to sets of batches and schedule these batches such that the total weighted tardiness costs and the total set-up costs are minimized. This problem is formulated as a mixed-integer nonlinear programming model and thus provides a challenging area for metaheuristics. We propose a hybrid algorithm of particle swarm optimization (PSO) and artificial immune algorithm (AIA) to solve this problem. In the proposed algorithm, a novel particle solution representation is designed for representing a batching scheme for SBPSP and the AIA mechanism improves the diversity of the swarm. Computational experiments on randomly generated instances with different structures show the validity and effectiveness of the proposed algorithm.

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Section: Proceedings Of the Ieeementioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

PSO algorithm for a single machine scheduling problem with batching in chemical industries

Yan

Tang

2009

2009 IEEE International Conference on Automation and Logistics

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“…In the context of classification, PSO is used for enhancing the classification accuracy rate of linear discriminant analysis [34]. Furthermore, it has been applied to a variety of tasks, such as the training of artificial neural networks [14,16,40,53]. PSO is also proposed in [28] as a new tool for Data Mining.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

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The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

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“…Reference [26] aimed to achieve minimization of FC, THDV and THDI. [27] and [28] employed passive filters for minimization of the objective function including FC, FL, THDI and THDV. In [29], four objectives as maximization of PF and minimization of FC, THDV and THDI are collectively considered to find optimal passive filter design.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the above mentioned approaches, [30] and [31] employed passive filters for minimization of the harmonic loss factor or maximization of transformer's loading capability under harmonically contaminated load current conditions. In these studies, the heuristic methods such as the differential evolution (DE) [13], [14], [22], [26], genetic algorithms [18], [19], [25] and particle swarm optimization method [15], [16], [27], [28] were extensively utilized to solve the optimal passive filter design problem. The most important advantage of the heuristic methods is that they provide a reasonable solution (near globally optimal) in a short time or less iterations [32].…”

Section: Introductionmentioning

confidence: 99%

Optimal design of single-tuned passive filters using Response Surface Methodology

Sakar

Karaoğlan

Balcı

et al. 2015

2015 International School on Nonsinusoidal Currents and Compensation (ISNCC)

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-This paper presents an approach based on Response Surface Methodology (RSM) to find the optimal parameters of the single-tuned passive filters for harmonic mitigation. The main advantages of RSM can be underlined as easy implementation and effective computation. Using RSM, the single-tuned harmonic filter is designed to minimize voltage total harmonic distortion (THDV) and current total harmonic distortion (THDI). Power factor (PF) is also incorporated in the design procedure as a constraint. To show the validity of the proposed approach, RSM and Classical Direct Search (Grid Search) methods are evaluated for a typical industrial power system.

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A PSO method with nonlinear time-varying evolution based on neural network for design of optimal harmonic filters

Cited by 48 publications

References 21 publications

PSO algorithm for a single machine scheduling problem with batching in chemical industries

PSO algorithm for a single machine scheduling problem with batching in chemical industries

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Optimal design of single-tuned passive filters using Response Surface Methodology

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