Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Masadeh, Raja; Sharieh, Ahmad; Jamal, Sanad; Qasem, Mais Haj; Al-Saaidah, Bayan

doi:10.14569/ijacsa.2020.0110913

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…However, common problems of heuristic search, such as being stuck at local maxima and plateau, sometimes occur in hill climbing. Here, we implemented a parallel steepest hill climbing search by dividing the predefined tolerance range into multiple subsections, which can partly mitigate local maximum and plateau problem, meanwhile allowing parallel multithreading search. , It will thus be more efficient than traversing search and could find more credible solution than the simple hill climbing. The pathway of the parallel steepest hill climbing search is illustrated in Figure B with the final optimum chemical shift tolerance (Δδ o ) marked by an asterisk.…”

Section: Methods and Algorithmmentioning

confidence: 99%

“…GIPMA algorithm proposed an adaptive method for intelligently selecting the optimum chemical shift tolerance (Δδ o ) in the predefined tolerance range to maximize the number of vPC by the algorithm of parallel hill climbing or traversing search . In artificial intelligence, hill climbing is a heuristic search algorithm, which allows a tradeoff between solution quality and search time .…”

Section: Methods and Algorithmmentioning

confidence: 99%

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GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D ¹H–¹³C HSQC-Based Metabolomics

Chen

et al. 2023

Anal. Chem.

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Two-dimensional (2D) 1 H− 13 C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1 H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1 H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1 H− 13 C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδ o ) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδ o for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1 H− 13 C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

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Section: Methods and Algorithmmentioning

confidence: 99%

Section: Methods and Algorithmmentioning

confidence: 99%

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D ¹H–¹³C HSQC-Based Metabolomics

Chen

et al. 2023

Anal. Chem.

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“…Furthermore, the authors in [49] proposed a sequential version of the hill-climbing algorithm and a parallel version using the Message Passing Interface (MPI) for finding the best path in the mountain environment. For comparing the two versions, they used three different sizes of datasets.…”

Section: A Finding Shortest Path Problemmentioning

confidence: 99%

Parallel Ant Colony Optimization Algorithm for Finding the Shortest Path for Mountain Climbing

et al. 2023

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The problem of finding the shortest path between two nodes is a common problem that requires a solution in many applications like games, robotics, and real-life problems. Since its deals with a large number of possibilities. Therefore, parallel algorithms are suitable to solve this optimization problem that has attracted a lot of researchers from both industry and academia to find the optimal path in terms of runtime, speedup, efficiency, and cost compared to sequential algorithms. In mountain climbing, finding the shortest path from the start node under the mountain to reach the destination node is a fundamental operator, and there are some interesting issues to be studied in mountain climbing that cannot be found in a traditional two-dimensional space search. We present a parallel Ant Colony Optimization (ACO) to find the shortest path in the mountain climbing problem using Apache Spark. The proposed algorithm guarantees the security of the selected path by applying some constraints that take into account the secure slope angle for the path. A generated dataset with variable sizes is used to evaluate the proposed algorithm in terms of runtime, speedup, efficiency, and cost. The experimental results show that the parallel ACO algorithm significantly (p < 0.05) outperformed the best sequential ACO. On the other hand, the parallel ACO algorithm is compared with one of the most recent research from the literature for finding the best path for mountain climbing problems using the parallel A* algorithm with Apache Spark. The parallel ACO algorithm with Spark significantly outperformed the parallel A* algorithm.

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The Usage of 3D Printing Technology by Small-Medium Sized Enterprise in Jordan

Masa’deh

Al-Dmour

Masadeh

et al. 2023

The Effect of Information Technology on Business and Marketing Intelligence Systems

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Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Cited by 6 publications

References 18 publications

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D ¹H–¹³C HSQC-Based Metabolomics

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D ¹H–¹³C HSQC-Based Metabolomics

Parallel Ant Colony Optimization Algorithm for Finding the Shortest Path for Mountain Climbing

The Usage of 3D Printing Technology by Small-Medium Sized Enterprise in Jordan

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Best Path in Mountain Environment based on Parallel Hill Climbing Algorithm

Cited by 6 publications

References 18 publications

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H–13C HSQC-Based Metabolomics

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H–13C HSQC-Based Metabolomics

Parallel Ant Colony Optimization Algorithm for Finding the Shortest Path for Mountain Climbing

The Usage of 3D Printing Technology by Small-Medium Sized Enterprise in Jordan

Contact Info

Product

Resources

About

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D ¹H–¹³C HSQC-Based Metabolomics

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D ¹H–¹³C HSQC-Based Metabolomics