A Quantum Behaved Gravitational Search Algorithm

The present paper proposes a supervised learning based automated human facial emotion recognition strategy with a feature selection scheme employing a novel variation of the gravitational search algorithm (GSA). The initial feature set is generated from the facial images by using the 2-D discrete cosine transform (DCT) and then the proposed modified binary quantum GSA with differential mutation (MBQGSA-DM) is utilized to select a sub-set of features with high discriminative power. This is achieved by minimising the cost function formulated as the ratio of the within class and interclass distances. The overall system performs its final classification task based on selected feature inputs, utilising a back propagation based artificial neural network (ANN). Extensive experimental evaluations are carried out utilising a standard, benchmark emotion database, that is, Japanese Female Facial Expresssion (JAFFE) database and the results clearly indicate that the proposed method outperforms several existing techniques, already known in literature for solving similar problems. Further validation has also been carried out on a facial expression database developed at Jadavpur University, Kolkata, India and the results obtained further strengthen the notion of superiority of the proposed method.Keywords: discrete cosine transform (DCT), gravitational search algorithm (GSA), modified binary quantum gravitational search algorithm with differential mutation (MBQGSA-DM), artificial neural network (ANN)

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“…Inspired by this concept provided in (Moghadam et al ., ), the final position update equation can be derived as

({, \begin{array}{c} center & X_{i} ((), t + 1) = italicXbes t_{i} ((), t) + λ . (||, italicXbes t_{i} ((), t) - X_{i} ((), t)) . normalln ((), \frac{1}{italicrand}) italicif R \geq 0.5 \\ center & X_{i} ((), t + 1) = italicXbes t_{i} ((), t) - λ . (||, italicXbes t_{i} ((), t) - X_{i} ((), t)) . normalln ((), \frac{1}{italicrand}) italicif R < 0.5 \end{array})

…”

Section: Feature Selection Using Mbqgsa‐dmmentioning

confidence: 99%

“…Inspired by this concept provided in (Moghadam et al, 2012), the final position update equation can be derived as Where rand and R are uniformly distributed random numbers in [0,1] and λ is the expansion-contraction coefficient. Here Xbest i (t) is computed using the following equation:…”

Section: Quantum-behaved Gravitational Search Algorithmmentioning

confidence: 99%

Automated emotion recognition employing a novel modified binary quantum‐behaved gravitational search algorithm with differential mutation

et al. 2015

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“…Their characteristics can be generalized as simplicity, flexibility, derivation‐free mechanism, and local optimal avoidance. Some of the well‐known meta‐heuristic algorithms were developed including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Artificial Bee Colony algorithm (ABC), Gravitational Search Algorithm (GSA), and Grey Wolf Optimizer (GWO) . However, heuristic‐based FORM methods have been underestimated in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

Structural reliability assessment by salp swarm algorithm–based FORM

Zhong

Wang

Dang

et al. 2020

Quality & Reliability Eng

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The first-order reliability method (FORM) is well recognized as an efficient approach for reliability analysis. Rooted in considering the reliability problem as a constrained optimization of a function, the traditional FORM makes use of gradient-based optimization techniques to solve it. However, the gradient-based optimization techniques may result in local convergence or even divergence for the highly nonlinear or high-dimensional performance function. In this paper, a hybrid method combining the Salp Swarm Algorithm (SSA) and FORM is presented. In the proposed method, a Lagrangian objective function is constructed by the exterior penalty function method to facilitate meta-heuristic optimization strategies. Then, SSA with strong global optimization ability for highly nonlinear and high-dimensional problems is utilized to solve the Lagrangian objective function. In this regard, the proposed SSA-FORM is able to overcome the limitations of FORM including local convergence and divergence. Finally, the accuracy and efficiency of the proposed SSA-FORM are compared with two gradient-based FORMs and several heuristic-based FORMs through eight numerical examples. The results show that the proposed SSA-FORM can be generally applied for reliability analysis involving low-dimensional, high-dimensional, and implicit performance functions. KEYWORDS first-order reliability method, high-dimensional problem, meta-heuristic algorithm, reliability analysis, Salp Swarm Algorithm 1224

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Section: Introductionmentioning

confidence: 99%

“…Based on the dynamics of Quantum, Mohadeseh Soleimanpour et al [20] proposed Quantum behaved GSA but it suffers with diversity loss problem in collecting the masses of objects. Later on, Improved Quantum behaved GSA is proposed in which fitness function of QGSA is replaced by new fitness function [21,22]. Radu et al [23,24] applied three modifications: define constraint regarding system, modify deprecation equation of gravitation constant and extended symmetrical method and proposed new GSA to reduce parametric sensitivity of fuzzy based control system for optimal tuning.…”

Section: Introductionmentioning

confidence: 99%

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

Singh¹,

Deep²

2015

IJISA

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Abstract-The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

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A Quantum Behaved Gravitational Search Algorithm

Cited by 25 publications

References 6 publications

Automated emotion recognition employing a novel modified binary quantum‐behaved gravitational search algorithm with differential mutation

Automated emotion recognition employing a novel modified binary quantum‐behaved gravitational search algorithm with differential mutation

Structural reliability assessment by salp swarm algorithm–based FORM

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

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