scholarly journals Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Ernesto Liñán-García ◽  
Lorena Marcela Gallegos-Araiza
Keyword(s):  
Simulated Annealing ◽  
Sequence Alignment ◽  
Simulated Annealing Algorithm ◽  
Multiple Sequence ◽  
Heating And Cooling ◽  
Dna Sequence Alignment ◽  
Alignment Problem ◽  
Simple Change ◽  
Annealing Algorithm

A new algorithm for solving sequence alignment problem is proposed, which is named SAPS (Simulated Annealing with Previous Solutions). This algorithm is based on the classical Simulated Annealing (SA). SAPS is implemented in order to obtain results of pair and multiple sequence alignment. SA is a simulation of heating and cooling of a metal to solve an optimization problem. In order to select randomly a current solution, SAPS algorithm chooses a solution from solutions that have been previously generated within the Metropolis Cycle. This simple change has led to increase the quality of the solution to the problem of aligning genomic sequences with respect to the classical Simulated Annealing algorithm. The parameters of SAPS, for certain instances, are tuned by an analytical method, and some parameters have experimentally been tuned. SAPS has generated high-quality results in comparison with the classical SA. The instances used are specific genes of the AIDS virus.

scholarly journals Near-optimal state assignment in a finite state machine

2021 ◽  
Vol 3 (1) ◽  
pp. 9-16
Author(s):  
Reinaldo Da Silva Ribeiro ◽  
Rafael Lima de Carvalho ◽  
Tiago Da Silva Almeida
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Systems Design ◽  
The State ◽  
Solution Quality ◽  
State Assignment ◽  
Finite State ◽  
Annealing Algorithm ◽  
The Impact

In this research, the application of the Simulated Annealing algorithm to solve the state assignment problem in finite state machines is investigated. The state assignment is a classic NP-Complete problem in digital systems design and impacts directly on both area and power costs as well as on the design time. The solutions found in the literature uses population-based methods that consume additional computer resources. The Simulated Annealing algorithm has been chosen because it does not use populations while seeking a solution. Therefore, the objective of this research is to evaluate the impact on the quality of the solution when using the Simulated Annealing approach. The proposed solution is evaluated using the LGSynth89 benchmark and compared with other approaches in the state-of-the-art. The experimental simulations point out an average loss in solution quality of 14.29%, while an average processing performance of 58.67%. The results indicate that it is possible to have few quality losses with a significant increase in processing performance.

scholarly journals A Simulated Annealing Algorithm to Construct Covering Perfect Hash Families

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Jose Torres-Jimenez ◽  
Idelfonso Izquierdo-Marquez
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Greedy Algorithms ◽  
Medium Size ◽  
Combinatorial Designs ◽  
Exploration And Exploitation ◽  
Covering Arrays ◽  
Annealing Algorithm ◽  
Perfect Hash Families

Covering perfect hash families (CPHFs) are combinatorial designs that represent certain covering arrays in a compact way. In previous works, CPHFs have been constructed using backtracking, tabu search, and greedy algorithms. Backtracking is convenient for small CPHFs, greedy algorithms are appropriate for large CPHFs, and metaheuristic algorithms provide a balance between execution time and quality of solution for small and medium-size CPHFs. This work explores the construction of CPHFs by means of a simulated annealing algorithm. The neighborhood function of this algorithm is composed of three perturbation operators which together provide exploration and exploitation capabilities to the algorithm. As main computational results we have the generation of 64 CPHFs whose derived covering arrays improve the best-known ones. In addition, we use the simulated annealing algorithm to construct quasi-CPHFs from which quasi covering arrays are derived that are then completed and postoptimized; in this case the number of new covering arrays is 183. Together, the 247 new covering arrays improved the upper bound of 683 covering array numbers.

Study on Hybrid Genetic Simulated Annealing Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

2010 ◽  
Vol 171-172 ◽  
pp. 167-170 ◽  
Author(s):  
Xiao Bo Wang ◽  
Jin Ying Sun ◽  
Chun Yu Ren
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Solution Quality ◽  
Genetic Simulated Annealing Algorithm ◽  
Algorithm Construct ◽  
Loading Problem ◽  
Annealing Algorithm ◽  
Crossover And Mutation

This paper studies multi-vehicle and multi-cargo loading problem under the limited loading capacity. Hybrid genetic simulated annealing algorithm is used to get the optimization solution. Firstly, adopt hybrid coding so as to make the problem more succinctly. On the basis of cubage-weight balance algorithm, construct initial solution to improve the feasibility. Adopt the improved non-uniform mutation so as to enhance local search ability of chromosomes. Secondly, through utilizing Boltzmann mechanism of simulated annealing algorithm, control crossover and mutation operation of genetic algorithm, search efficiency so as to improve the solution quality of algorithm. Finally, the example can be shown that the above model and algorithm is effective and can provide for large-scale ideas to solve practical problems.

scholarly journals A simulated annealing algorithm for the allocation of production resources in the food catering industry

2020 ◽  
Vol 122 (7) ◽  
pp. 2139-2158 ◽  
Author(s):  
Alessandro Tufano ◽  
Riccardo Accorsi ◽  
Riccardo Manzini
Keyword(s):  
Simulated Annealing ◽  
Food Safety ◽  
Food Quality ◽  
Simulated Annealing Algorithm ◽  
Production Plant ◽  
Content Type ◽  
Multi Objective ◽  
Catering Industry ◽  
Annealing Algorithm

PurposeThis paper addresses the trade-off between asset investment and food safety in the design of a food catering production plant. It analyses the relationship between the quality decay of cook-warm products, the logistics of the processes and the economic investment in production machines.Design/methodology/approachA weekly cook-warm production plan has been monitored on-field using temperature sensors to estimate the quality decay profile of each product. A multi-objective optimisation model is proposed to (1) minimise the number of resources necessary to perform cooking and packing operations or (2) to maximise the food quality of the products. A metaheuristic simulated annealing algorithm is introduced to solve the model and to identify the Pareto frontier of the problem.FindingsThe packaging buffers are identified as the bottleneck of the processes. The outcome of the algorithms highlights that a small investment to design bigger buffers results in a significant increase in the quality with a smaller food loss.Practical implicationsThis study models the production tasks of a food catering facility to evaluate their criticality from a food safety perspective. It investigates the tradeoff between the investment cost of resources processing critical tasks and food safety of finished products.Social implicationsThe methodology applies to the design of cook-warm production. Catering companies use cook-warm production to serve school, hospitals and companies. For this reason, the application of this methodology leads to the improvement of the quality of daily meals for a large number of people.Originality/valueThe paper introduces a new multi-objective function (asset investment vs food quality) proposing an original metaheuristic to address this tradeoff in the food catering industry. Also, the methodology is applied and validated in the design of a new food production facility.

scholarly journals Penerapan algoritma simulated annealing pada notifikasi informasi jadwal menggunakan RESTFUL api untuk rekomendasi jadwal asistensi di laboratorium komputer

2019 ◽  
Vol 9 (2) ◽  
pp. 91-101
Author(s):  
Anggra Triawan ◽  
Muhammad Al Faruq
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
School Science ◽  
High School Science ◽  
Unequal Distribution ◽  
Computer Laboratory ◽  
Laboratory Assistant ◽  
Annealing Algorithm ◽  
Feasibility Test

In the current era of globalization, technology is needed in the quality of high school science. The quality of knowledge influences relevant knowledge, ways of teaching, a technology used, and planning of courses. At STIKOM there are many courses that require computer laboratories, where these courses will not be effective if taught in theory. Every one or more courses conducted by a computer laboratory are taught by a lecturer and a laboratory. Assistant, if the Lecturer cannot teach Lab. The assistant will replace as a teacher according to the instructions given by the Lecturer. The problem is the difficulty in determining a suitable time schedule for the Lab. Missing assistants, like Lab. Assistant who is still in college or has a job. Unequal distribution of subjects in each Lab. Assistant. Therefore we need a system to provide scheduling recommendations for lab assistants. Simulated Annealing Algorithm uses the input of the ability of time from each lab assistant. and is chosen based on the amount of energy added if selected in one course and if the same amount of energy will be chosen randomly. The results of the feasibility test questionnaire conducted obtained proper results on users with a percentage of eligibility of 76.3% and very feasible on system experts with a percentage of eligibility of 90%.

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