Ant lion optimisation algorithm for structural damage detection using vibration data

Mishra, Mayank; Barman, Swarup K.; Maity, Damodar; Maiti, Dipak Kumar

doi:10.1007/s13349-018-0318-z

Cited by 68 publications

(36 citation statements)

References 77 publications

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“…In fact, the direction will open numerous opportunities for real life applications. Additionally, the RBFNN parameters can be optimized by integrating robust Metaheuristic algorithms such as Ant Lion Optimization approach [52], Harmony Search [53], modified Flower Pollination Algorithm [54], fine-tuning Metaheuristics [55] and particle swarm optimization algorithm [56]. The simulation also provides evidence that RBFNN can obtain the optimized weight by optimally connecting the hidden layer and output layer.…”

Section: Discussionmentioning

confidence: 99%

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

et al. 2020

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Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal hidden neuron, especially when dealing with Boolean Satisfiability logical rule. In this paper, we present a comprehensive investigation of the potential effect of systematic Satisfiability programming as a logical rule, namely 2 Satisfiability (2SAT) to optimize the output weights and parameters in RBFNN. The 2SAT logical rule has extensively applied in various disciplines, ranging from industrial automation to the complex management system. The core impetus of this study is to investigate the effectiveness of 2SAT logical rule in reducing the computational burden for RBFNN by obtaining the parameters in RBFNN. The comparison is made between RBFNN and the existing method, based on the Hopfield Neural Network (HNN) in searching for the optimal neuron state by utilizing different numbers of neurons. The comparison was made with the HNN as a benchmark to validate the final output of our proposed RBFNN with 2SAT logical rule. Note that the final output in HNN is represented in terms of the quality of the final states produced at the end of the simulation. The simulation dynamic was carried out by using the simulated data, randomly generated by the program. In terms of 2SAT logical rule, simulation revealed that RBFNN has two advantages over HNN model: RBFNN can obtain the correct final neuron state with the lowest error and does not require any approximation for the number of hidden layers. Furthermore, this study provides a new paradigm in the field feed-forward neural network by implementing a more systematic propositional logic rule.

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Section: Discussionmentioning

confidence: 99%

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

et al. 2020

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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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“…The error function is based on a combination of the FRF and frequency, which are functions of the damage index. The objective function used in this study derived from Mohan et al [27] and Mishra et al [38] is given as follows:…”

Section: Formulation Of the Frfmentioning

confidence: 99%

“…Consequently, the heuristic approaches have gained popularity in the past few years. Several swarmbased heuristic approaches, such as the genetic algorithm (GA) [18][19][20][21][22], simulated annealing (SA) [23,24], tabu search [25], standard particle swarm optimisation (SPSO) [26][27][28][29], unified particle swarm optimisation (UPSO) [30,31], artificial bee colony (ABC) [32,33], ant colony optimisation (ACO) [34][35][36], charged system search [37], and ant lion optimisation [38], as well as their hybrid versions [39][40][41][42] have been used in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Structural health monitoring is crucial for the timely damage diagnosis of civil infrastructure. This paper explores the damage detection method based on the ant colony algorithm (ACO) by using Hooke-Jeeves (HJ) pattern search for intensification. The HJ is incorporated into the ACO to improve its performance in detecting damages. The damage is simulated by reducing the stiffness of the structural members, via elastic modulus reduction factor. Four civil engineering structures of varying complexity are analysed for low-and high-level damage scenarios to test the efficacy of the proposed approach. An inverse problem is formulated to minimise the objective function based on the frequency response function rather than using the frequency and mode-shape-based approach. The analysis results indicate that the proposed method can locate damages and identify their severity with higher precision than previously used GA, SPSO, and UPSO can.

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Ant lion optimisation algorithm for structural damage detection using vibration data

Cited by 68 publications

References 77 publications

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network

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

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

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