Penguin Search Optimisation Algorithm for Finding Optimal Spaced Seeds

2015 ◽  
Vol 7 (2) ◽  
pp. 85-99 ◽  
Author(s):  
Youcef Gheraibia ◽  
Abdelouahab Moussaoui ◽  
Youcef Djenouri ◽  
Sohag Kabir ◽  
Peng-Yeng Yin ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Computation Time ◽  
Software Tools ◽  
Evolutionary Relationships ◽  
Metaheuristic Algorithm ◽  
Biological Sequences ◽  
Matching Methods ◽  
Optimisation Algorithm ◽  
Optimal Subset ◽  
Spaced Seeds

This paper develops PeSeeD, a new metaheuristic algorithm for finding optimal spaced seed. Sequences matching is a hot topic in bio-informatics, which is used in many applications such as understanding the functional, structural, or evolutionary relationships between the sequences. The most relevant sequences matching methods are based on seeds designed to match two biological sequences. The first approach which introduced seeds was facilitated via Blastn tool, the approach builds seeds of 11 length size. However, it is clear that not all local alignments have to include an identical fragment of length 11. The spaced seeds approach is one of the methods which does not require a consecutive matching position. Dynamic programming is used to solve this kind of problem and it takes quadratic time. Several approaches have then been proposed to improve the sensitivity of searching in reasonable runtime. To reduce the complexity of such approaches, other heuristics based approaches can also be reviewed. The aim is to find spaced seeds subset which improves sensitivity without increasing the computation time. In this paper, the optimal subset spaced seeds are explored using the bio-inspired approach, penguins search optimisation algorithm (‘'PeSOA'' for short). The authors further propose an efficient heuristic for computing the overlap complexity between seeds. To evaluate the efficiency of the proposed approach, they compared the obtained results with the results of several seeds based software tools. The obtained results are very promising in terms of sensitivity and computation time for the overlap complexity.

A Brief Review on Software Tools in Generating Chou’s Pseudo-factor Representations for All Types of Biological Sequences

2018 ◽  
Vol 25 (9) ◽  
pp. 822-829 ◽  
Author(s):  
Wei Zhao ◽  
Likun Wang ◽  
Tian-Xiang Zhang ◽  
Ze-Ning Zhao ◽  
Pu-Feng Du
Keyword(s):  
Software Tools ◽  
Biological Sequences

A Improved Stereo Matching Fast Algorithm Based on Dynamic Programming

2012 ◽  
Vol 531-532 ◽  
pp. 657-661
Author(s):  
Zi Wei Zhou ◽  
Ge Li ◽  
Chang Le Li ◽  
Ji Zhuang Fan
Keyword(s):  
Dynamic Programming ◽  
Stereo Matching ◽  
Computation Time ◽  
Local Algorithm ◽  
Color Feature ◽  
Line Segments ◽  
Gaussian Pyramid ◽  
Scan Line ◽  
Disparity Space Image ◽  
Disparity Space

Compared with the local algorithm in stereo matching, the high quality disparity space image is calculated by the global algorithm, which is difficult to use in practical application for its long computation time. The dynamic programming is one of the global algorithms with a fast matching speed, but it has strip blemish in matching result. In this paper, a new dynamic programming based method is proposed to accelerate the matching speed and improve the matching quality. Firstly, the color feature of two images are calculated using the Laplacians of Gaussian pyramid algorithm, and the color feature of the image pair obtained are matched. Secondly, the matching points are taken as the ground control points of the scan line, which is cut into several short line segments. Finally, all line segments are matched to obtain the disparity of the scan line. The experimental results show that the matching speed is accelerated greatly with improved disparity image quality

Improving Manufacturing Agility Through Optimal Production Scheduling System

2021 ◽  
Vol 7 (1) ◽  
pp. 48-52
Author(s):  
Moch Saiful Umam ◽  
Mustafid ◽  
Suryono
Keyword(s):  
Production Scheduling ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Local Optimum ◽  
Metaheuristic Algorithm ◽  
Scheduling System ◽  
Product Cycles ◽  
Global Industry ◽  
Local Searches

The garment industry is a global industry that requires high agility in response to changing market demands that are quickly changing. Short product cycles with unpredictable demand often make the industry unable to meet consumer needs. In increasing the agility of production to deliver products to customers as fast as possible, the production scheduling system must be designed optimally. Recently algorithm hybridization is used because the combination of more than one algorithm is more optimal. Genetic Algorithm (GA) is a metaheuristic algorithm is applied in various production scheduling and its power can be improved by combining it with the Tabu Search (TS). The GA is the best metaheuristic algorithm to output the optimal scheduling with less execution time but has the disadvantage –easily trapped in local optimum (early convergence is faster). The TS algorithm works as a local search algorithm with a faster computation time than GA. This study aims to minimize the total time to complete the work (minimizing makespan) by combining TS into GA in conducting local searches to increase industrial agility. The results obtained are GA-TS hybridization can provide a more optimal solution for the production scheduling in the garment so that agility can increase.

Control Design for Automated Vehicle Emergency Safe Stop System Based on Differential Dynamic Programming

2020 ◽  
Author(s):  
Lisheng Yang ◽  
Tomonari Furukawa ◽  
Lei Zuo ◽  
Zachary Doerzaph
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Control Algorithm ◽  
Computation Time ◽  
Grid Cell ◽  
System Control ◽  
Real Time Control ◽  
Differential Dynamic Programming ◽  
Time Control ◽  
Emergency Stop

Abstract This paper presents the control algorithm and system design for a newly proposed automated emergency stop system, which aims to navigate the vehicle out of its travel lane to a safe road-side location when an emergency (e.g. driver fails to take control during fallback of the Dynamic Driving Task) occurs. To address the unique requirements of such a system, control techniques based on differential dynamic programming are developed. Optimal control sequence computation is broken down into step-by-step quadratic optimization and solved iteratively. Control constraints are addressed efficiently by a tailored Projected-Newton algorithm. The iterative control algorithm is then integrated into a real-time control system which considers both computation delay and modeling errors. The system employs a novel grid-based storage structure for recording all acceptable control commands computed within the iteration and uses a high frequency estimator for self-localization. During operation, the real-time control thread will extract commands from the grid cell corresponding to current states. Simulation results show strong potential of the proposed system for addressing the engineering challenges of the automated emergency stop function. The robustness of the system in presence of computation time delay and modelling errors is also demonstrated.

Dynamic Programming Approach to Load Estimation Using Optimal Sensor Placement and Model Reduction

2018 ◽  
Vol 15 (08) ◽  
pp. 1850071 ◽  
Author(s):  
Deepak K. Gupta ◽  
Anoop K. Dhingra
Keyword(s):  
Inverse Problem ◽  
Dynamic Programming ◽  
Model Reduction ◽  
Structural Response ◽  
Computation Time ◽  
Dynamic Programming Method ◽  
Programming Approach ◽  
Continuous Systems ◽  
Programming Technique ◽  
Design Algorithm

A time-domain technique for estimating dynamic loads acting on a structure from structural response measured experimentally at a finite number of optimally placed sensors on the structure is presented. The technique relies on an existing solution method based on dynamic programming, which consists of a backward (inverse) time sweeping phase followed by a forward time sweeping phase. The dynamic programming method of load identification, similar to all other inverse methods, suffers from ill-conditioning. Small variations (noise) in response measurements can cause large errors in load estimates. The condition of the inverse problem, and hence the quality of load estimates, depends on the locations of sensors on the structure. There can be a large number of locations on a structure where sensors can potentially be mounted. A D-optimal design algorithm is used to arrive at optimal sensor locations such that the condition of the inverse problem is improved and precise load estimates are obtained. Another major limitation of the dynamic programming technique is that the computation time increases dramatically as the model size increases. To deal with this shortcoming, a technique based on Craig–Bampton model reduction is also proposed in this paper. Numerical results illustrate the effectiveness of the proposed technique in accurately recovering the loads imposed on discrete as well as continuous systems.

scholarly journals An improved heuristic-dynamic programming algorithm for rectangular cutting problem

2020 ◽  
Vol 17 (3) ◽  
pp. 717-735
Author(s):  
Aihua Yin ◽  
Chong Chen ◽  
Dongping Hu ◽  
Jianghai Huang ◽  
Fan Yang
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Programming Algorithm ◽  
Recursive Method ◽  
Two Dimensional ◽  
One Dimensional ◽  
Block Width ◽  
Cutting Problem

In this paper, the two-dimensional cutting problem with defects is discussed. The objective is to cut some rectangles in a given shape and direction without overlapping the defects from the rectangular plate and maximize some profit associated. An Improved Heuristic-Dynamic Program (IHDP) is presented to solve the problem. In this algorithm, the discrete set contains not only the solution of one-dimensional knapsack problem with small rectangular block width and height, but also the cutting positions of one unit outside four boundaries of each defect. In addition, the denormalization recursive method is used to further decompose the sub problem with defects. The algorithm computes thousands of typical instances. The computational experimental results show that IHDP obtains most of the optimal solution of these instances, and its computation time is less than that of the latest literature algorithms.

scholarly journals Efficient Solution Algorithms for Factored MDPs

2003 ◽  
Vol 19 ◽  
pp. 399-468 ◽  
Author(s):  
C. Guestrin ◽  
D. Koller ◽  
R. Parr ◽  
S. Venkataraman
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Central Element ◽  
Dynamic Bayesian Network ◽  
Computation Time ◽  
Programming Algorithm ◽  
Small Subset ◽  
Planning Under Uncertainty ◽  
State Variables ◽  
Solution Algorithms

This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model using a dynamic Bayesian network. This representation often allows an exponential reduction in the representation size of structured MDPs, but the complexity of exact solution algorithms for such MDPs can grow exponentially in the representation size. In this paper, we present two approximate solution algorithms that exploit structure in factored MDPs. Both use an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. A key contribution of this paper is that it shows how the basic operations of both algorithms can be performed efficiently in closed form, by exploiting both additive and context-specific structure in a factored MDP. A central element of our algorithms is a novel linear program decomposition technique, analogous to variable elimination in Bayesian networks, which reduces an exponentially large LP to a provably equivalent, polynomial-sized one. One algorithm uses approximate linear programming, and the second approximate dynamic programming. Our dynamic programming algorithm is novel in that it uses an approximation based on max-norm, a technique that more directly minimizes the terms that appear in error bounds for approximate MDP algorithms. We provide experimental results on problems with over 10^40 states, demonstrating a promising indication of the scalability of our approach, and compare our algorithm to an existing state-of-the-art approach, showing, in some problems, exponential gains in computation time.

scholarly journals Acceleration of Nucleotide Semi-Global Alignment with Adaptive Banded Dynamic Programming

2017 ◽  
Author(s):  
Hajime Suzuki ◽  
Masahiro Kasahara
Keyword(s):  
Dynamic Programming ◽  
Single Molecule ◽  
Computation Time ◽  
Error Rates ◽  
Nucleotide Sequences ◽  
Sequencing Error ◽  
Local Alignment ◽  
Global Alignment ◽  
Alignment Algorithm ◽  
Short Read

AbstractMotivationPairwise alignment of nucleotide sequences has previously been carried out using the seed- and-extend strategy, where we enumerate seeds (shared patterns) between sequences and then extend the seeds by Smith-Waterman-like semi-global dynamic programming to obtain full pairwise alignments. With the advent of massively parallel short read sequencers, algorithms and data structures for efficiently finding seeds have been extensively explored. However, recent advances in single-molecule sequencing technologies have enabled us to obtain millions of reads, each of which is orders of magnitude longer than those output by the short-read sequencers, demanding a faster algorithm for the extension step that accounts for most of the computation time required for pairwise local alignment. Our goal is to design a faster extension algorithm suitable for single-molecule sequencers with high sequencing error rates (e.g., 10-15%) and with more frequent insertions and deletions than substitutions.ResultsWe propose an adaptive banded dynamic programming algorithm for calculating pairwise semi-global alignment of nucleotide sequences that allows a relatively high insertion or deletion rate while keeping band width relatively low (e.g., 32 or 64 cells) regardless of sequence lengths. Our new algorithm eliminated mutual dependences between elements in a vector, allowing an efficient Single-Instruction-Multiple-Data parallelization. We experimentally demonstrate that our algorithm runs approximately 5× faster than the extension alignment algorithm in NCBI BLAST+ while retaining similar sensitivity (recall).We also show that our extension algorithm is more sensitive than the extension alignment routine in DALIGNER, while the computation time is comparable.AvailabilityThe implementation of the algorithm and the benchmarking scripts are available at https://github.com/ocxtal/[email protected]

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