Multiverse Optimisation Algorithm for Capturing the Critical Slip Surface in Slope Stability Analysis

Mishra, Mayank; Ramana, G. V.; Maity, Damodar

doi:10.1007/s10706-019-01037-2

Cited by 29 publications

(15 citation statements)

References 44 publications

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“…The results are a slightly on higher side when compared with CS, CS-BC, BBO and ICA, reason can be due to the method used to calculate FS as the difference is almost negligible. The standard deviation is obtained with ALO (0.162) is smaller than the one obtained by BBO (0.384) [46] but slightly more than MVO (0.1229) [49]. Figure 10 reports the critical slip surface obtained by solving the benchmark example by using the current [92] Genetic algorithm 1.288 Jongmin et al [93] Limit equilibrium with the velocity field and plastic zone 1.37 Zhu et al [94] A generalised framework of LEM 1.373 Kashani et al [48] Imperialistic Competitive Algorithm (ICA) 1.3625 Mishra et al [50] Teaching-learning-based optimisation (TLBO) 1.315-1.466 ALO (This study)…”

Section: Examplementioning

confidence: 58%

“…Zolfaghari et al [95] Genetic algorithm (GA) 1.24 Cheng et al [26] Simulated annealing (SA) 1.2813 Cheng et al [87] Tabu search (TS) 1.4661 Cheng et al [87] Ant colony optimisation (ACO) 1.5817 Cheng et al [87] Harmony search (HS) 1.2405 Cheng et al [87] Modified harmony search (MHS) 1.1315 Cheng et al [24] Modified Particle swarm optimisation (MPSO) 1.1289 Cheng et al [20] Particle swarm optimisation (PSO) 1.1095 Khajehzadeh et al [30] Gravitational search algorithm 1.0785 Kang et al [27] Artificial bee colony optimisation (ABC) 1.086 Gandomi et al [28] Firefly algorithm (FA) 1.303 Gandomi et al [28] Cuckoo search (CS) 1.0635 Gandomi et al [28] Cuckoo search-boundary constraint (CS-BC) 1.0502 Gandomi et al [46] Biogeography-based optimization (BBO) 1.055 Gandomi et al [46] Differential evolution (DE) 1.659 Gandomi et al [46] Evolutionary strategy (ES) 1.502 Mishra et al [49] Multi verse optimiser (MVO) 1.1447-1.7362 ALO (This study)…”

Section: Source Methods Fsmentioning

confidence: 99%

“…Goh [92] Genetic algorithm (GA) 1.387 Bolton et al [97] Leap frog method 1.387 Cheng et al [87] Particle swarm optimisation (PSO) 1.359 Cheng et al [87] Ant colony optimisation (ACO) 1.3931 Cheng et al [87] Simulated annealing (SA) 1.3569 Cheng et al [87] Tabu search (TS) 1.3762 Cheng el al. [20] Harmony search algorithm (HSA) 1.359 Kang et al [27] Artificial bee colony optimisation (ABC) 1.343 Gao [45] Evolutionary programming (EP) 1.358 Gao [45] Immunised evolutionary programming (IEP) 1.355 Gao [22] Ant colony optimisation (ACO) 1.353 Gao [23] Meeting ant colony optimisation (MACO) 1.348 Gao [22] Premium penalty ant colony optimisation (PPACO) 1.340 Goh [22] Finite element method 1.426 Gao [98] Improved black hole algorithm 1.355 Xiao et al [43] Enhanced fireworks algorithm (EFW) 1.350 Mishra et al [50] Teaching-learning-based optimisation (TLBO) 1.343-1.356 Mishra et al [49] Multi verse optimiser (MVO) 1.346-1.376 ALO (This study)…”

Section: Source Methods Fsmentioning

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%

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Slope stability analysis using recent metaheuristic techniques: a comprehensive survey

2019

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In the framework of designing civil engineering structures such as dams and road embankments, slope stability assessment is essential. Many slope stability assessment methods based on swarm intelligence algorithms and artificial intelligence techniques have been developed in the past decade. Therefore, this paper aims to provide an up-to-date overview of slope stability by summarizing the review of applications of several metaheuristics in this field with their advantages and limitations. In this study, we present recent swarm intelligence methods and machine learning techniques used for assessing the stability of slopes and compare them with the antlion optimiser (ALO) technique. The factor of safety related to every slip surface searched was computed using Morgenstern-Price method. The performance of the proposed ALO is evaluated and validated for four slope examples against recent metaheuristics. Finally, the work carried out will help practitioners as they can now have all swarm intelligence approaches combined in one paper and will help in choosing them the right technique based on their application.

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Section: Examplementioning

confidence: 58%

Section: Source Methods Fsmentioning

confidence: 99%

Section: Source Methods Fsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Slope stability analysis using recent metaheuristic techniques: a comprehensive survey

2019

Self Cite

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“…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.…”

Section: Related Workmentioning

confidence: 99%

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

2019

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In the modern world, wireless body area networks (WBAN) is projected to play a vital role in biomedical and psychological applications. The practical implementation of WBAN technology surfer from various deployment issues that have to be dealt with. The more serious concern is associated with the energy consumption of these networks. Biosensor nodes continuously sense the signals and send the same to sink. Sending data to sink is an energy-consuming operation, so routing is done to optimize energy utilization in WBAN. The continuous data sensing and the transmission of information over long-distances result in huge energy consumption of these nodes. So, conservation of energy is the need of the hour. The main focus of the current study is to invise a routing mechanism that makes use of particle swarm optimization based on metaheuristic algorithm along with the relay node selection based on distances and residual energies. Experimental results show that the proposed protocol strikes a perfect balance between minimizing the number of relay nodes (to be positioned on subject) along with the energy efficient WBAN.

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A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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Multiverse Optimisation Algorithm for Capturing the Critical Slip Surface in Slope Stability Analysis

Cited by 29 publications

References 44 publications

Slope stability analysis using recent metaheuristic techniques: a comprehensive survey

Slope stability analysis using recent metaheuristic techniques: a comprehensive survey

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

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

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