Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints

Pothiya, S.; Ngamroo, Issarachai; Kongprawechnon, Waree

doi:10.1016/j.enconman.2007.08.012

Cited by 192 publications

(97 citation statements)

References 14 publications

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“…Untuk mengatasi masalah ini, beberapa metode heuristik telah terbukti keberhasilannya dalam menangani permasalahan ini yaitu constriction factor based particle swarm optimization (CFBPSO) dan kombinasi inertia weight contriction factor (IWCFPSO) [3], hybrid simulated annealing particle swarm optimization (SA-PSO) [4], multiple tabe search (MTS) [5], chaotic ant swarm optimization (CASO) [6], imperialist competitive algorithm (ICA) [7][8], extention particle swarm optimization (E-PSO) [9], differential evolution ant colony optimization (DE-ACO) [10], genetic algorithm (GA) [4]. Keunggulan dari metode GA terletak pada proses seleksi dan evaluasi yaitu crossover.…”

Section: Pendahuluanunclassified

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Pembangkitan Ekonomis pada Unit Pembangkit Listrik Tenaga Diesel Telaga Gorontalo Menggunakan Algoritma Genetika

Kanata¹

2017

JRE

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Section: Pendahuluanunclassified

“…Algoritma ini memperkenalkan mekanisme tambahan seperti inisialisasi, pencarian adaptif, crossover, dan proses restart. Metode ini diuji pada kasus 6 pembangkitan sistem tenaga IEEE 26 bus dan menunjukkan hasil lebih baik dibandingkan metode simulated annealing (SA), GA, tabu search (TS), dan PSO [5].…”

Section: Studi Pustakaunclassified

Pembangkitan Ekonomis pada Unit Pembangkit Listrik Tenaga Diesel Telaga Gorontalo Menggunakan Algoritma Genetika

Kanata¹

2017

JRE

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“…This technique is a novel socio-politically motivated global search scheme that is currently induced in order to deal with various optimization tasks. In [29], this algorithm is utilized to detect the optimized weights of the ANN. ICA has major performances to be mentioned, such as rapid convergence and superior global minimum attainment.…”

Section: Imperialist Competitive Algorithm Methodsmentioning

confidence: 99%

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

Razvarz

Jafari

2017

MCA

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This paper discusses the elimination of Colour Index Acid Yellow 23 (C.I. AY23) using the ultraviolet (UV)/Ag-TiO 2 process. To anticipate the photocatalytic elimination of AY23 with the existence of Ag-TiO 2 nanoparticles processed under desired circumstances, two computational techniques, namely artificial neural network (ANN) and imperialist competitive algorithm (ICA) modeling are developed. A sum of 100 datasets are used to establish the models, wherein the introductory concentration of dye, UV light intensity, initial dosage of nano Ag-TiO 2 , and irradiation time are the four parameters expressed in the form of input variables. Additionally, the elimination of AY23 is considered in the form of the output variable. Out of the 100 datasets, 80 are utilized in order to train the models. The remaining 20 that were not included in the training are used in order to test the models. The comparison of the predicted outcomes extracted from the suggested models and the data obtained from the experimental analysis validates that the performance of the ANN scheme is comparatively sophisticated when compared with the ICA scheme.

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“…In this case, the load demand expected to be determined was PD=1263 MW. The B matrix of the transmission loss coefficient is given by: Table 7 shows the performance comparison among the proposed algorithms, a particle swarm optimization (PSO) approach [10], a novel string based GA [7], standard ge-netic algorithm (GA) method [10], multiple tabu search algorithm (MTS) [8], and the simulated annealing (SA) method [9]. The simulation results of the proposed approach outperformed recent optimization methods presented in the literature in terms of solution quality and time convergence.…”

Section: Test Systemmentioning

confidence: 99%

“…To accelerate the search process authors in [8] proposed a multiple tabu search algorithm (MTS) to solve the dynamic economic dispatch (ED) problem with generator constraints, simulation results prove that this approach is able to reduce the computational time compared to the conventional approaches. Authors in [9] present an algorithm based simulated annealing to solve the optimal power flow.…”

Section: Introductionmentioning

confidence: 99%

Economic Power Dispatch with Discontinuous Fuel Cost Functions using Improved Parallel PSO

Mahdad¹,

Bouktir²,

Srairi³

et al. 2010

Journal of Electrical Engineering and Technology

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-This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, MTS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

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Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints

Cited by 192 publications

References 14 publications

Pembangkitan Ekonomis pada Unit Pembangkit Listrik Tenaga Diesel Telaga Gorontalo Menggunakan Algoritma Genetika

Pembangkitan Ekonomis pada Unit Pembangkit Listrik Tenaga Diesel Telaga Gorontalo Menggunakan Algoritma Genetika

ICA and ANN Modeling for Photocatalytic Removal of Pollution in Wastewater

Economic Power Dispatch with Discontinuous Fuel Cost Functions using Improved Parallel PSO

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