2018 4th International Conference on Recent Advances in Information Technology (RAIT) 2018
DOI: 10.1109/rait.2018.8389049
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Analysis of slope stability and detection of critical failure surface using gravitational search algorithm

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Cited by 8 publications
(3 citation statements)
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“…Algorithmic specific parameters for each algorithm with their numerical values used in slope stability analysis by different researchers ACO [21] Ant colony size (300), total number of tours (200), pheromone evaporation rate ( = 0.3 ), bilinear scaling constant Z = 2 for the quality function PSO [24] Inertial weight = 0.5 , swarm size (60), cognitive parameter c 1 =2 and social parameter c 2 = 2 , number of iterations (200) SA [26] Initial temperature state, cooling rate, acceptance probability, maximum number of iterations ABC [27] Bee colony size (20-50), number of cycles (100), limit (300-650) FA [28] Swarm of n = 50 particles, parameter representing attractiveness, light absorption coefficient ( ) varying between 0.1 and 10, generation = 3000; FSO [29] Fish pool f p = 0.9 , number of fishes in f p , parameter for optimisation pr = 0.1 , probability array = 0.8, maximum number of iterations = 700 GSA [30,31] Population size N = 50 , initial gravitational constant G 0 = 100 , constant = 0.1 , maximum iteration t max = 1000 BBO [32] Number of habitats (100), mutation rate M max = 0.2 , maximum immigration and emigration rate I = 0.5 and E = 1 , number of generations (250) BB-BC [33] Universe composed of N b = 30 number of bodies, search space reduction parameter =0.7-0.9, maximum size of the step = 1 , scaling factor = 0.7 , generation number =50, distribution constants = 1.4, = 0.3 GA [37] Crossover (0.75) and mutation probability (0.002), position of crossover, length of chromosome (24), population size (200), number of generations (300) FWA [43] Number of spark seeds N = 10 , number of generating sparks M = 40 , maximum explosion amplitude  = 40 , number of Gaussian sparks M e = 5 , total number of iterations (2000) BHA [44] Population of stars (50), maximum number of iterations (500) DE [46] Weighting factor F, crossover constant C r , size of population (50), maximum number of generations (3000) ES [46] Size of population (50), maximum number of generations (3000), number of offspring , standard deviation ICA [48] Population of countries (300), population of imperialists (8) and decades (500), rate of revolution (0.3), damp ratio (0.99), uniting threshold (0.02), control parameter = 0.1, = 2, = 0.02 HS [52] Harmony memory consideration rate HR = 0.98 , the pitch adjustment rate PR = 0.1 , harmony memory HM of size M, number of function evaluations NOFs TLBO [50] Number of learners and maximum number of iterations ALO (proposed approach)…”
Section: Algorithmmentioning
confidence: 99%
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“…Algorithmic specific parameters for each algorithm with their numerical values used in slope stability analysis by different researchers ACO [21] Ant colony size (300), total number of tours (200), pheromone evaporation rate ( = 0.3 ), bilinear scaling constant Z = 2 for the quality function PSO [24] Inertial weight = 0.5 , swarm size (60), cognitive parameter c 1 =2 and social parameter c 2 = 2 , number of iterations (200) SA [26] Initial temperature state, cooling rate, acceptance probability, maximum number of iterations ABC [27] Bee colony size (20-50), number of cycles (100), limit (300-650) FA [28] Swarm of n = 50 particles, parameter representing attractiveness, light absorption coefficient ( ) varying between 0.1 and 10, generation = 3000; FSO [29] Fish pool f p = 0.9 , number of fishes in f p , parameter for optimisation pr = 0.1 , probability array = 0.8, maximum number of iterations = 700 GSA [30,31] Population size N = 50 , initial gravitational constant G 0 = 100 , constant = 0.1 , maximum iteration t max = 1000 BBO [32] Number of habitats (100), mutation rate M max = 0.2 , maximum immigration and emigration rate I = 0.5 and E = 1 , number of generations (250) BB-BC [33] Universe composed of N b = 30 number of bodies, search space reduction parameter =0.7-0.9, maximum size of the step = 1 , scaling factor = 0.7 , generation number =50, distribution constants = 1.4, = 0.3 GA [37] Crossover (0.75) and mutation probability (0.002), position of crossover, length of chromosome (24), population size (200), number of generations (300) FWA [43] Number of spark seeds N = 10 , number of generating sparks M = 40 , maximum explosion amplitude  = 40 , number of Gaussian sparks M e = 5 , total number of iterations (2000) BHA [44] Population of stars (50), maximum number of iterations (500) DE [46] Weighting factor F, crossover constant C r , size of population (50), maximum number of generations (3000) ES [46] Size of population (50), maximum number of generations (3000), number of offspring , standard deviation ICA [48] Population of countries (300), population of imperialists (8) and decades (500), rate of revolution (0.3), damp ratio (0.99), uniting threshold (0.02), control parameter = 0.1, = 2, = 0.02 HS [52] Harmony memory consideration rate HR = 0.98 , the pitch adjustment rate PR = 0.1 , harmony memory HM of size M, number of function evaluations NOFs TLBO [50] Number of learners and maximum number of iterations ALO (proposed approach)…”
Section: Algorithmmentioning
confidence: 99%
“…At present, intelligent algorithms such as harmony search (HS) [20], ant colony optimisation (ACO) [21][22][23], particle swarm optimisation (PSO) [24,25], simulated annealing (SA) [26], artificial bee colony (ABC) [27], cuckoo search (CS) [28], firefly algorithm (FA) [28], fish swarm optimisation (FSO) [29], gravitational search algorithm (GSA) [30][31][32], big bang -big crunch (BB-BC) optimisation [33], relevance vector machine [34], mutative scaled chaos (MSC) [35], tabu search (TS) [36], genetic algorithms (GA) [37][38][39][40][41][42], fireworks algorithm (FWA) [43], black hole algorithm (BHA) [44], immunised evolutionary programming (IEP) [45], differential evolution (DE) [46,47], evolutionary strategy (ES) [46,47], and biogeography-based optimisation (BBO) [46,47], imperialistic competitive algorithm (ICA) [48], multiverse optimisation algorithm (MVO) [49] and teaching-learning-based optimisation (TLBO) [50] have been applied for slope stability analysis. Moreover, the hybridisation of these algorithms, such as CS with boundary constraint (CS-EB) [51], PSO with HS (PSO-HS) [52], ACO with simulated annealing (ACO-SA) [53], and GSA with sequential quadratic programming (GSA-SQP) …”
Section: Introductionmentioning
confidence: 99%
“…Many efforts have been made to analyze bank slope stability under the influence of excavation [9][10][11][12], earthquakes [13][14][15], seismic activity [16], rainfall [17][18][19][20], and freezethaw effects [21,22], especially the reservoir water level change [23,24]. But very few studies have compared the stability before and after GSP.…”
Section: Introductionmentioning
confidence: 99%