Distribution Network Reconfiguration with Different Load Models using Adaptive Quantum inspired Evolutionary Algorithm

A Comprehensive Review of Metaheuristic Methods for the Reconfiguration of Electric Power Distribution Systems and Comparison With a Novel Approach Based on Efficient Genetic Algorithm

“…The proposed method (EGA) and IGA were applied to a number of test systems and the results were compared with the solutions obtained by other GA approaches: standard GA [22], [46], RGA [47], GAMT [49], restricted GA (ReGA) [50], DGA [51], SOReco [52], FGA [54], [59], [60], FAGA [55], NSGA [56], fast NSGA (FNSGA) [61], and nonrevisiting GA (NrGA) [62]. Comparisons also include classic No Recombination operator is applied as described in ( 29) and (30).…”

“…Content may change prior to final publication. methods, heuristic approaches, and metaheuristic algorithms such as SA [13], [14], [63], ESA [15], gravitational SA (GSA) [22], TS [16], ITS [17], MTS [18], EA [20], [64], [65], DEA [21], variable scaling DEA (VSDEA) [66], FEA [23], [24], PSO [22], [25], MPSO [27], EIPSO [28], selfadaptive PSO (SAPSO) [64], ACO [29], [67], HC-ACO [30], [68], AACO [31], HACO [32], fuzzy ACO (FACO) [69], AIS [33], [34], BFOA [35], HSA [36], TLBO [38], BB-BC [39], honey bee mating optimization (HBMO) [70], [71], shuffled frog leaping algorithm (SFLA) [64], dragonfly [72], and grey wolf optimizer (GWO) [22]. Available parameters of meta-heuristic approaches used for comparison with proposed method have been given in Appendix.…”

“…However, EA-based methods highly depend on chose of the operator values and suffer high computational time, stagnation, and premature convergence. In order to overcome these drawbacks, an adaptive quantum inspired EA (adaptive QiEA) was proposed in [22] for power loss minimization through DSR. The QiEA method by integrating some roles of quantum mechanics into EA algorithm, establishes good balance between exploration and exploitation.…”

“…  1 2, 6,8 Y    2 1, 7, 6 Y  (16)   3 1, 6,8 Y  Y1 proposes that switches 2 (normal branch), 6 (tie line), and 8 (normal branch) have to be open and others (switches 1, 3, 4, 5, 7, and 9) should be closed.The branch currents and nodal voltage magnitudes (│Iij│and│Vi│) of radial topologies (chromosomes which satisfy radiality constraint (9)) are computed by backwardforward sweep load flow (radial power flow). Using radial power flow in metaheuristic algorithms is a common way in reconfiguration studies, because of its simplicity and shorter computational time, compared to conventional power flow methods[22]. In the proposed power flow, load currents are computed iteratively with the updated voltages of each bus, while currents summation is calculated in the backward way and voltage drops are determined by the forward sweep from far end to sending end of a radial feeder/lateral.…”

Adaptive quantum-inspired evolutionary algorithm for optimizing power losses by dynamic load allocation on distributed generators

“…Moreover, AQiEA doesn't require any other operators like mutations or local heuristics to drive the individuals toward convergence. Recently, AQiEA is applied to some engineering optimization problems like ceramic grinding [4], optimal location and size of Distributed Generators (DG) [5], Network Reconfiguration [6], Siting and sizing of Capacitors [7], Cost analysis of DG & Capacitor [8] and simultaneous implementation of both DGs and capacitors [9], and has shown better performances as compared with other approaches.…”