2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) 2017
DOI: 10.1109/wiecon-ece.2017.8468889
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Implementation of ABC Algorithm To Solve Simultaneous Substation Expansion And Transmission Expansion Planning

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Cited by 4 publications
(7 citation statements)
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“…From the viewpoint of optimization methods to provide optimal schemes in a computationally tractable manner, the previous studies can be classified as metaheuristic-and mathematical-based ones. The first class of references applies approaches like real genetic algorithm (GA) [8,9], decimal codification GA [47,48], non-dominated sorting genetic algorithm II (NSGA II) [17,18], particle swarm optimization (PSO) [10][11][12]29], improved discrete PSO [49,50], a combination of PSO and GA (PSO+GA) [27], differential evolution (DE) [13][14][15][16], DE-continuous population-based incremental learning (DE-PBILC) [24,25], artificial bee colony (ABC) [46], modified ABC [19][20][21], discrete ABC [52], improved discrete ABC [51], ant colony optimization algorithm for continuous domains (ACOR) [23], a combination of firefly algorithm and harmonic search algorithm (FFA+HAS) [39], adaptive tabu search (TS) [53], and hybridization of GA, TS, and artificial neural network (GA + TS + ANN) [28]. The latter one uses interior point method (IPM) [7], primal-dual interior-point method (PDIPM) [26], benders decomposition [34,38,40,44] or describes MINLP [22,36], mixed-integer linear programming (MILP) [30-33, 39, 41, 45, 52], and mixed-integer second-order cone programming (MISOCP) …”
Section: Motivation and Backgroundmentioning
confidence: 99%
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“…From the viewpoint of optimization methods to provide optimal schemes in a computationally tractable manner, the previous studies can be classified as metaheuristic-and mathematical-based ones. The first class of references applies approaches like real genetic algorithm (GA) [8,9], decimal codification GA [47,48], non-dominated sorting genetic algorithm II (NSGA II) [17,18], particle swarm optimization (PSO) [10][11][12]29], improved discrete PSO [49,50], a combination of PSO and GA (PSO+GA) [27], differential evolution (DE) [13][14][15][16], DE-continuous population-based incremental learning (DE-PBILC) [24,25], artificial bee colony (ABC) [46], modified ABC [19][20][21], discrete ABC [52], improved discrete ABC [51], ant colony optimization algorithm for continuous domains (ACOR) [23], a combination of firefly algorithm and harmonic search algorithm (FFA+HAS) [39], adaptive tabu search (TS) [53], and hybridization of GA, TS, and artificial neural network (GA + TS + ANN) [28]. The latter one uses interior point method (IPM) [7], primal-dual interior-point method (PDIPM) [26], benders decomposition [34,38,40,44] or describes MINLP [22,36], mixed-integer linear programming (MILP) [30-33, 39, 41, 45, 52], and mixed-integer second-order cone programming (MISOCP) …”
Section: Motivation and Backgroundmentioning
confidence: 99%
“…In available researches for NEP and G-NEP such as [47][48][49][50][51][52][53][54][55][56], making an expansion decision relies on a list of candidate substations and transmission lines using cost-effective analysis. However, multiple transformers with different capacities and voltage ratios can be installed in new and existing substations according to geographical limitations.…”
Section: Motivation and Backgroundmentioning
confidence: 99%
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