A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem

Niknam, Taher

doi:10.1016/j.apenergy.2009.05.016

Cited by 245 publications

(102 citation statements)

References 33 publications

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“…The modified fuzzy PSO (MFPSO) algorithm proposed by the authors combines the previously described concepts developed by Alfi, Fateh [2] and Niknam [3]. In this algorithm, each of particles has its own coefficients ω, c 1 and c 2 changing according to the linguistic description represented by the fuzzy rules.…”

Section: Mfpso: Authors' Propositionmentioning

confidence: 99%

“…FPSO3: algorithm based on the work by Niknam [3] In the FPSO3 algorithm, the fuzzy system proposed by Niknam [3] is used to change not only ω, but also the coefficients c 1 and c 2 . These coefficients determine the influence of the personal best position pbest i and the global best position gbest on the particle velocity.…”

Section: Proceedings Of the Fedcsis Gdańsk 2016mentioning

confidence: 99%

“…The most common are algorithms with a constriction factor [9] and with a linear inertia weight [10]. Among the PSO modifications we can distinguish algorithms that utilize fuzzy systems [2], [3], [11]- [15]. For example, in papers [2], [11] a fuzzy system was used to dynamically modify the inertia weight.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in papers [2], [11] a fuzzy system was used to dynamically modify the inertia weight. Another approach was presented in [3], where a fuzzy system is used to change the inertia weight and the coefficients of the cognitive and social components.…”

Section: Introductionmentioning

confidence: 99%

“…The usefulness of PSO in solving a wide range of optimization problems has been repeatedly confirmed. It has been applied to: the intelligent identification and control of a dynamic system [2]; solving an economic dispatch problem in power systems [3]; human motion tracking [4]; feature selection [5]; automatic incident detection [6]; fuzzy anomaly detection in networks [7]; the estimation of hurdles clearance parameters [8] and many more problems. Many variants of the PSO have been developed since it was introduced in 1995 [1].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of selected fuzzy particle swarm optimization algorithms

Krzeszowski

Wiktorowicz

2016

Annals of Computer Science and Information Systems

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Abstract-This paper is devoted to an evaluation of selected fuzzy particle swarm optimization algorithms. Two non-fuzzy and four fuzzy algorithms are considered. The Takagi-Sugeno fuzzy system is utilized to change the parameters of these algorithms. A modified fuzzy particle swarm optimization method is proposed, in which each of the particles has its own inertia weight and coefficients of the cognitive and social components. The evaluation is based on the common nonlinear benchmark functions used for testing optimization methods. The ratings of the algorithms are assigned on the basis of the mean of the objective function and the relative success.

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Section: Mfpso: Authors' Propositionmentioning

confidence: 99%

Section: Proceedings Of the Fedcsis Gdańsk 2016mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of selected fuzzy particle swarm optimization algorithms

Krzeszowski

Wiktorowicz

2016

Annals of Computer Science and Information Systems

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Flow cytometric analysis of genetic FRET detectors containing variable substrate sequences

Lim

Hsu

Park

2010

Biotechnology Progress

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A genetic Fluorescence Resonance Energy Transfer (FRET) detector undergoes a post-translational modification (PTM)-induced conformational change that results in increased FRET. To test if the PTM-dependent FRET change can be quantified by flow cytometry, we purified and immobilized a genetic detector on microbeads and used flow cytometry to measure its FRET efficiency before and after Erk-2-mediated phosphorylation. The fluorescence ratio R between the acceptor and donor fluorescence, which was obtained by fitting a straight line through the data points in linear space, increases following phosphorylation, thus demonstrating that flow cytometry is capable of detecting a PTM-dependent FRET response. Furthermore, when Erk-2 and a genetic detector are coexpressed in bacteria, the measured R value changes with the substrate sequence with near single residue resolution. Similarly, the cells coexpressing the glycosylating enzyme O-GlcNAc transferase (OGT) and a genetic detector specific for OGT exhibit a PTM-induced change in FRET efficiency. Therefore, the combination of flow cytometry and a genetic detector may be useful to characterize the substrate specificity of a PTM enzyme and identify the sequences that are preferentially targeted for PTM in vivo.

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A New Optimization Framework for Harmonic Compensation Considering Plug‐in Electric Vehicle Penetration Using Adaptive Particularly Tunable Fuzzy Chaotic Particle Swarm Optimization

2021

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Plug‐in electric vehicles (PEVs) can contribute to eliminating undesirable harmonics generated by nonlinear loads. In this study, a novel stochastic optimization approach for harmonic compensation is proposed which is capable of optimizing contrary objectives, including total harmonic distortion and harmonic inject current, simultaneously, while meeting the relevant constraints. This problem can be influenced by the uncertainty of PEVs which is reflected in the force outage rate concept. The Monte–Carlo simulation technique is implemented to consider the uncertainty associated with PEVs by generating plausible scenarios with the aim of converting the mentioned framework to the respective deterministic equivalents. Afterward, adaptive particularly tunable fuzzy chaotic particle swarm optimization (APTFCPSO) is utilized, based on the weighted sum method, and the acquired results are compared with those obtained by other implemented swarm intelligence‐based algorithms. Accordingly, at first, several benchmark optimization functions are considered to verify the performance of the APTFCPSO. Afterward, active power line conditioners (APLCs) and PEVs are separately employed for harmonics cancellation in the deterministic form. After adopting the scenario reduction technique, the optimization framework is solved for each remaining scenario by the mentioned procedure. The statistical analysis reveals that PEVs outperform APLCs to cancel harmonic orders defined in a 14‐node micro‐grid.

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A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem

Cited by 245 publications

References 33 publications

Evaluation of selected fuzzy particle swarm optimization algorithms

Evaluation of selected fuzzy particle swarm optimization algorithms

Flow cytometric analysis of genetic FRET detectors containing variable substrate sequences

A New Optimization Framework for Harmonic Compensation Considering Plug‐in Electric Vehicle Penetration Using Adaptive Particularly Tunable Fuzzy Chaotic Particle Swarm Optimization

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