Gradient gravitational search: An efficient metaheuristic algorithm for global optimization

Dash, Tirtharaj; Sahu, Prabhat Kumar

doi:10.1002/jcc.23891

Cited by 18 publications

(9 citation statements)

References 58 publications

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“…However, other algorithms, such as evolutionary algorithm and basin‐hopping algorithm, are also used as strategies of geometrical improvements. It would be very interesting to apply SGMS‐HMGP to other systems, multicomponent Lennard‐Jones atomic clusters, water clusters, and off‐lattice protein models . These applications are helpful to evaluate the size‐guided multi‐seed algorithm.…”

Section: Discussionmentioning

confidence: 99%

Size‐guided multi‐seed heuristic method for geometry optimization of clusters: Application to benzene clusters

Takeuchi

2018

J Comput Chem

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Since searching for the global minimum on the potential energy surface of a cluster is very difficult, many geometry optimization methods have been proposed, in which initial geometries are randomly generated and subsequently improved with different algorithms. In this study, a size-guided multi-seed heuristic method is developed and applied to benzene clusters. It produces initial configurations of the cluster with n molecules from the lowest-energy configurations of the cluster with n - 1 molecules (seeds). The initial geometries are further optimized with the geometrical perturbations previously used for molecular clusters. These steps are repeated until the size n satisfies a predefined one. The method locates putative global minima of benzene clusters with up to 65 molecules. The performance of the method is discussed using the computational cost, rates to locate the global minima, and energies of initial geometries. © 2018 Wiley Periodicals, Inc.

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Section: Discussionmentioning

confidence: 99%

Size‐guided multi‐seed heuristic method for geometry optimization of clusters: Application to benzene clusters

Takeuchi

2018

J Comput Chem

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“…The conformational search techniques are achieved by three different force fields: CHARMM, AMBER, and OPLS-AA . As described earlier, a small value of the tuning parameter has been adopted, σ = [0, 1], i.e., 0.001, to deal with conformational search implementation and to obtain computational accuracy at low cost.…”

Section: Methodsmentioning

confidence: 99%

Conformational Search for the Building Block of Proteins Based on the Gradient Gravitational Search Algorithm (ConfGGS) Using Force Fields: CHARMM, AMBER, and OPLS-AA

Pradhan

Panigrahi

Sahu

2023

J. Chem. Inf. Model.

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Proteins are linear polymers built from a repertoire of 20 different amino acids, which are considered building blocks of proteins. The diversity and versatility of these 20 building blocks with regard to their conformations are key to adopting three-dimensional structures that facilitate proteins to undergo important mechanistic biological processes in living systems. The present investigation reports a conformational search of 20 different amino acids, building blocks of proteins, using three different force fields, CHARMM, AMBER, and OPLS-AA, implemented in the gradient gravitational search algorithm. The search technique (ConfGGS) includes the contribution from both bonded and nonbonded terms using Cartesian coordinates. The efficiency of such conformational searches has also been compared with other optimization algorithms: DE/Best, DE/Rand, and PSO algorithms with respect to computational time and accuracy based on the minimum number of iteration steps and computed lowest mean absolute error (MAE) and mean standard deviation (MSD) values for dihedral angles of respective near-optimal structures. Moreover, the ConfGGS technique has also been extended to an ordered protein fragment (PQITL) extracted from HIV-1 protease (PDB ID: 1YTH), an intrinsically disordered protein fragment, i.e., an amyloid-forming segment (AVVTGVTAV), from the NAC domain of Parkinson’s disease protein α-synuclein, residues 69–77 (PDB ID: 4RIK), the experimental NMR atomic-resolution structure of α-synuclein fibrils (PDB ID: 2N0A), and a disulfide bond-containing protein fragment sequence (PCYGWPVCY), residues 59–67 (PDB ID: 6Y4F) toward structure prediction as a close homologue compared with experimental accuracy, using the CHARMM force field. The MolProbity validation results for the protein fragment (PQITL) obtained by ConfGGS/CHARMM are in better agreement with the native protein fragment structure of HIV-1 protease (PDB ID: 1YTH). Furthermore, the computed results have also been compared with the coordinates obtained from the AlphaFold network.

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“…Swain and Khilar [22] and Swain et al [23] used a combination of statistical and soft computing approaches for heterogeneous faults detection and classification in the sensor network. Usages of metaheuristic optimisation algorithms [24] for modelling neural networks [25,26] have achieved significant focus in the field of machine learning. Such an approach was introduced in [27] for detecting various kinds of faults in sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Lightweight approach to automated fault diagnosis in WSNs

2020

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Automated fault diagnosis in wireless sensor networks (WSNs) has always been an essential field for the networks research community. In resource-related constraints, the design of a lightweight method to automatically diagnose the fault in WSN is highly beneficial. In this study, the authors propose lightweight and less-overhead approaches to diagnose hard and soft faults in WSNs automatically. Correctly, a lightweight checksum method is implemented for hard or crash fault detection. This method is capable of detecting multiple hard faults within a single path with the help of a timeout mechanism. For diagnosis of soft faults such as permanent, intermittent, and transient faults, they implement the Anderson-Darling (AD) statistical method. The AD test analyses how the sensor readings are fitted in a specific distribution for a tested significance level. Several testbed experiments are conducted to validate their hypotheses and implementations. The performance parameters that are evaluated in this work include fault detection accuracy, false alarm rate, and false-positive rate. These parameters are evaluated about various fault probabilities in the network. They observe that the proposed lightweight schemes can diagnose both hard and soft faults in O(1) message complexity over the network, which makes it adaptable in practise.

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Gradient gravitational search: An efficient metaheuristic algorithm for global optimization

Cited by 18 publications

References 58 publications

Size‐guided multi‐seed heuristic method for geometry optimization of clusters: Application to benzene clusters

Size‐guided multi‐seed heuristic method for geometry optimization of clusters: Application to benzene clusters

Conformational Search for the Building Block of Proteins Based on the Gradient Gravitational Search Algorithm (ConfGGS) Using Force Fields: CHARMM, AMBER, and OPLS-AA

Lightweight approach to automated fault diagnosis in WSNs

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