Teaching–learning-based optimisation algorithm and its application in capturing critical slip surface in slope stability analysis

Slope stability analysis using recent metaheuristic techniques: a comprehensive survey

“…Yamagami and Ueta [88] Broyden-Fletcher-Goldfarb-Shanno (BFGS) 1.338 Yamagami and Ueta [88] Simplex method 1.339-1.348 Cheng et al [87] Particle swarm optimisation (PSO) 1.329 Cheng et al [87] Modified particle swarm optimisation (MPSO) 1.326 Cheng et al [20] Modified harmony search (MHS) 1.322 Jianping et al [89] Genetic algorithm (GA) + line 1.324 Jianping et al [89] Genetic algorithm (GA) + Spline 1.321 Kahatadeniya et al [21] Ant colony optimisation (ACO) 1.311-2.966 Khajehzadeh et al [25] Particle swarm optimisation (PSO) 1.321 Khajehzadeh et al [25] Modified particle swarm optimisation (MPSO) 1.308 Kang et al [27] Artificial bee colony optimisation (ABC) 1.321 Kashani et al [48] Imperialistic competitive algorithm (ICA) 1.321 Xiao et al [43] Enhanced fireworks algorithm (EFW) 1.322 RS slope [90] Cuckoo search 1.327 Mishra et al [50] Teaching-learning-based optimisation (TLBO) 1.324-1.325 ALO (This study) Antlion Optimiser (ALO) NP = 10 1.334-1.387 ALO (This study) Antlion Optimiser (ALO) NP = 50 1.317-1.349 Fig. 7 Factor of safety for fittest antlion versus number of iterations by using populations of 50 and 10 for example 1 Figure 8 presents the slip surface for populations of 10 and 100 identified using the current technique.…”

“…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)…”

“…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) …”

PSOBAN: a novel particle swarm optimization based protocol for wireless body area networks

“…Energy-efficient routing protocols like topology-based WBAN [26], multi-hop based WBAN [27], medium access control based WBAN [28,29], and priority-based WBAN [30] are proposed by the authors. In order to reduce the consumption of energy in an efficient manner, various optimization algorithms [31][32][33][34][35][36] have been explored in the area of wireless technology [37][38][39][40][41][42][43][44][45]. However, such algorithms don't focus much on WBAN.…”

Structural health monitoring based on the hybrid ant colony algorithm by using Hooke–Jeeves pattern search

“…Several linear-nonlinear, local-global, and stochastic-deterministic techniques have been used to obtain the solution of the objective function [16]. Traditional deterministic approaches cannot correctly identify damages because they get trapped in multiple local minima and are timeconsuming [17]. Almost all conventional optimisation methods do not guarantee the global minima and are highly sensitive to the initial conditions.…”