scholarly journals A New Implementation of Genome Rearrangement Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoqian Jing ◽  
Haihe Shi
Keyword(s):  
Simulated Annealing ◽  
Branch And Bound ◽  
Genome Rearrangement ◽  
Greedy Algorithms ◽  
Practical Application ◽  
Component Library ◽  
Greedy Strategy ◽  
Assembly Algorithm ◽  
Rearrangement Algorithm ◽  
Biological Similarity

Unsigned reverse genome rearrangement is an important part of bioinformatics research, which is widely used in biological similarity and homology analysis, revealing biological inheritance, variation, and evolution. Branch and bound, simulated annealing, and other algorithms in unsigned reverse genome rearrangement algorithm are rare in practical application because of their huge time and space consumption, and greedy algorithms are mostly used at present. By deeply analyzing the domain of unsigned reverse genome rearrangement algorithm based on greedy strategy (unsigned reverse genome rearrangement algorithm (URGRA) based on greedy strategy), the domain features are modeled, and the URGRA algorithm components are interactively designed according to the production programming method. With the support of the PAR platform, the algorithm component library of the URGRA is formally realized, and the concrete algorithm is generated by assembly, which improves the reliability of the assembly algorithm.

Component Rearrangement on Printed Wiring Boards to Maximize the Fundamental Natural Frequency

1993 ◽  
Vol 115 (3) ◽  
pp. 312-321 ◽  
Author(s):  
Tien-Sheng Chang ◽  
E. B. Magrab
Keyword(s):  
Simulated Annealing ◽  
Boundary Conditions ◽  
Natural Frequency ◽  
Printed Wiring Board ◽  
Computationally Efficient ◽  
Additional Increase ◽  
Simulated Annealing Method ◽  
Fundamental Natural Frequency ◽  
Rearrangement Algorithm ◽  
Annealing Method

A methodology to attain the highest fundamental natural frequency of a printed wiring board by rearranging its components has been developed. A general two-dimensional rearrangement algorithm is developed by which the rearrangement of the component-lead-board (CLB) assemblies is performed automatically for any combination of equal size, unequal size, movable and immovable CLBs. This algorithm is also capable of incorporating two design restrictions: fixed (immovable) components and prohibited (non-swappable) areas. A highly computationally efficient objective function for the evaluation of the automatic rearrangement process is introduced, which is a linear function of the size of the individual CLBs that have been selected for each interchange. The simulated annealing method is adapted to solve the combinatorial rearrangement of the CLBs. Using 61 combinations of boundary conditions, equal and unequal sized CLBs, movable and immovable CLBs, various CLB groupings and sets of material properties, it is found that, when compared to the exact solution obtained by an exhaustive search method, the simulated annealing method obtained the highest fundamental natural frequency within 1 percent for 87 percent of the cases considered, within 0.5 percent for 72 percent of the cases and the true maximum in 43 percent of them. To further increase the fundamental natural frequency the introduction of a single interior point support is analyzed. Depending on the boundary conditions an additional increase in the maximum fundamental natural frequency of 44 to 198 percent can be obtained.

scholarly journals Combining Interval Branch and Bound and Stochastic Search

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Dhiranuch Bunnag
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Branch And Bound ◽  
Lower Cost ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Constrained Problems ◽  
Interval Branch And Bound ◽  
Unconstrained Problems

This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen’s algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA.

scholarly journals Binary Sparse Phase Retrieval via Simulated Annealing

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Peng ◽  
Hongxia Wang
Keyword(s):  
Fourier Transform ◽  
Simulated Annealing ◽  
Recovery Rate ◽  
Phase Retrieval ◽  
High Recovery ◽  
Phase Recovery ◽  
Greedy Strategy ◽  
High Recovery Rate ◽  
Binary Model ◽  
The Fourier Transform

This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR) algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.

scholarly journals A Single-Machine Two-Agent Scheduling Problem by a Branch-and-Bound and Three Simulated Annealing Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shangchia Liu ◽  
Wen-Hsiang Wu ◽  
Chao-Chung Kang ◽  
Win-Chin Lin ◽  
Zhenmin Cheng
Keyword(s):  
Simulated Annealing ◽  
Branch And Bound ◽  
Single Machine ◽  
Completion Time ◽  
Optimal Solution ◽  
Total Completion Time ◽  
Practical Applications ◽  
Processing Resource ◽  
Agent Scheduling ◽  
Annealing Algorithms

In the field of distributed decision making, different agents share a common processing resource, and each agent wants to minimize a cost function depending on its jobs only. These issues arise in different application contexts, including real-time systems, integrated service networks, industrial districts, and telecommunication systems. Motivated by its importance on practical applications, we consider two-agent scheduling on a single machine where the objective is to minimize the total completion time of the jobs of the first agent with the restriction that an upper bound is allowed the total completion time of the jobs for the second agent. For solving the proposed problem, a branch-and-bound and three simulated annealing algorithms are developed for the optimal solution, respectively. In addition, the extensive computational experiments are also conducted to test the performance of the algorithms.

scholarly journals Simulated Annealing-Based Krill Herd Algorithm for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gai-Ge Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi ◽  
Hong Duan
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Greedy Strategy ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Low Probability ◽  
Elitism Scheme ◽  
Reliability And Robustness

Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.

scholarly journals Research on airport multi-objective optimization of stand allocation based on simulated annealing algorithm

2021 ◽  
Vol 18 (6) ◽  
pp. 8314-8330
Author(s):  
Ningning Zhao ◽  
◽  
Mingming Duan
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Objective Function ◽  
Simulated Annealing Algorithm ◽  
Allocation Scheme ◽  
Multi Objective Optimization ◽  
Practical Application ◽  
Allocation Decision ◽  
Multi Objective ◽  
Annealing Algorithm

<abstract> <p>In this study, a multi-objective optimized mathematical model of stand pre-allocation is constructed with the shortest travel distance for passengers, the lowest cost for airlines and the efficiency of stand usage as the overall objectives. The actual data of 12 flights at Lanzhou Zhongchuan Airport are analyzed by application and solved by simulated annealing algorithm. The results of the study show that the total objective function of the constructed model allocation scheme is reduced by 40.67% compared with the actual allocation scheme of the airport, and the distance traveled by passengers is reduced by a total of 4512 steps, while one stand is saved and the efficiency of stand use is increased by 31%, in addition to the reduction of airline cost by 300 RMB. In summary, the model constructed in the study has a high practical application value and is expected to be used for airport stand pre-allocation decision in the future.</p> </abstract>

A practical application of simulated annealing to clustering

1992 ◽  
Vol 25 (4) ◽  
pp. 401-412 ◽  
Author(s):  
Donald E. Brown ◽  
Christopher L. Huntley
Keyword(s):  
Simulated Annealing ◽  
Practical Application

scholarly journals Gene Sequence Assembly Algorithm Model Based on the DBG Strategy and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haihe Shi ◽  
Gang Wu
Keyword(s):  
Gene Sequence ◽  
Sequence Assembly ◽  
Feature Model ◽  
De Bruijn Graph ◽  
Specific Sequence ◽  
Component Library ◽  
Assembly Algorithm ◽  
Assembly Algorithms ◽  
Abstract Algorithm ◽  
Domain Level

With the continuous development of sequencing technology, the amount of bioinformatics data has increased geometrically, and the massive amount of bioinformatics data puts forward more stringent requirements for sequence assembly problems. The sequence assembly algorithm based on DBG (De Bruijn graph) strategy is a key algorithm in bioinformatics, which is widely used in the domain of gene sequence assembly. Current research on the domain of sequence assembly always focuses on optimization of specific steps to a specific algorithm and lack of research on domain-level high-abstract algorithm frameworks. To some extent, it leads to the redundancy of the sequence assembly algorithm, and some problems may be caused by the artificial selection algorithm. This paper analyzes the domain of DBGSA and establishes a feature model of this domain. Based on the production programming method, the DBGSA algorithm component is interactively designed. With the support of the PAR platform, the DBGSA algorithm component library is formally implemented, and furthermore, the DBGSA component library is used to assemble the specific algorithm. This research adds domain-level research to the domain of sequence assembly and implements the DBGSA component library, which can assemble specific sequence assembly algorithms, ensuring the efficiency of algorithm development and the reliability of assembly generation algorithms. At the same time, it also provides a valuable reference for solving problems in the domain of sequence assembly.

scholarly journals An improved approximation algorithm for the reversal and transposition distance considering gene order and intergenic sizes

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Klairton L. Brito ◽  
Andre R. Oliveira ◽  
Alexsandro O. Alexandrino ◽  
Ulisses Dias ◽  
Zanoni Dias
Keyword(s):  
Approximation Algorithm ◽  
Genetic Information ◽  
Genome Rearrangement ◽  
Simulated Data ◽  
Genetic Changes ◽  
Greedy Strategy ◽  
Approximation Factor ◽  
Transposition Event ◽  
A Genome ◽  
Intergenic Regions

Abstract Background In the comparative genomics field, one of the goals is to estimate a sequence of genetic changes capable of transforming a genome into another. Genome rearrangement events are mutations that can alter the genetic content or the arrangement of elements from the genome. Reversal and transposition are two of the most studied genome rearrangement events. A reversal inverts a segment of a genome while a transposition swaps two consecutive segments. Initial studies in the area considered only the order of the genes. Recent works have incorporated other genetic information in the model. In particular, the information regarding the size of intergenic regions, which are structures between each pair of genes and in the extremities of a linear genome. Results and conclusions In this work, we investigate the sorting by intergenic reversals and transpositions problem on genomes sharing the same set of genes, considering the cases where the orientation of genes is known and unknown. Besides, we explored a variant of the problem, which generalizes the transposition event. As a result, we present an approximation algorithm that guarantees an approximation factor of 4 for both cases considering the reversal and transposition (classic definition) events, an improvement from the 4.5-approximation previously known for the scenario where the orientation of the genes is unknown. We also present a 3-approximation algorithm by incorporating the generalized transposition event, and we propose a greedy strategy to improve the performance of the algorithms. We performed practical tests adopting simulated data which indicated that the algorithms, in both cases, tend to perform better when compared with the best-known algorithms for the problem. Lastly, we conducted experiments using real genomes to demonstrate the applicability of the algorithms.

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