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.

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 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 SIMULATED ANNEALING ALGORITHM FOR FEATURE SELECTION

2015 ◽  
Vol 15 (2) ◽  
pp. 6471-6479
Author(s):  
Francisca Rosario ◽  
Dr. K. Thangadurai
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Weather Data ◽  
Computational Technique ◽  
Slow Cooling ◽  
Solution Quality ◽  
Data Set ◽  
Annealing Algorithm

In the process of physical annealing, a solid is heated until all particles randomly arrange themselves forming the liquid state. A slow cooling process is then used to crystallize the liquid. This process is known as simulated annealing. Simulated annealing is stochastic computational technique that searches for global optimum solutions in optimization problems. The main goal here is to give the algorithm more time in the search space exploration by accepting moves, which may degrade the solution quality, with some probability depending on a parameter called temperature. In this discussion the simulated annealing algorithm is implemented in pest and weather data set for feature selection and it reduces the dimension of the attributes through specified iterations.

scholarly journals Optimal Facility Location Model Based on Genetic Simulated Annealing Algorithm for Siting Urban Refueling Stations

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Dawei Chen ◽  
Shun Zhou ◽  
Yuanchang Xie ◽  
Xuhong Li
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Impact Factors ◽  
Location Decisions ◽  
Second Stage ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Two Stages ◽  
Station Location ◽  
The Impact

This paper analyzes the impact factors and principles of siting urban refueling stations and proposes a three-stage method. The main objective of the method is to minimize refueling vehicles’ detour time. The first stage aims at identifying the most frequently traveled road segments for siting refueling stations. The second stage focuses on adding additional refueling stations to serve vehicles whose demands are not directly satisfied by the refueling stations identified in the first stage. The last stage further adjusts and optimizes the refueling station plan generated by the first two stages. A genetic simulated annealing algorithm is proposed to solve the optimization problem in the second stage and the results are compared to those from the genetic algorithm. A case study is also conducted to demonstrate the effectiveness of the proposed method and algorithm. The results indicate the proposed method can provide practical and effective solutions that help planners and government agencies make informed refueling station location decisions.

An evaluation of the simulated annealing algorithm for solving the area-restricted harvest-scheduling model against optimal benchmarks

2005 ◽  
Vol 35 (10) ◽  
pp. 2500-2509 ◽  
Author(s):  
Kevin A Crowe ◽  
J D Nelson
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Computing Time ◽  
Multiple Time ◽  
Harvest Scheduling ◽  
Problem Size ◽  
Solution Quality ◽  
Annealing Algorithm ◽  
Objective Function Values ◽  
Problem Instances

A common approach for incorporating opening constraints into harvest scheduling is through the area-restricted model. This model is used to select which stands to include in each opening while simultaneously determining an optimal harvest schedule over multiple time periods. In this paper we use optimal benchmarks from a range of harvest scheduling problem instances to test a metaheuristic algorithm, simulated annealing, that is commonly used to solve these problems. Performance of the simulated annealing algorithm was assessed over a range of problem attributes such as the number of forest polygons, age-class distribution, and opening size. In total, 29 problem instances were used, ranging in size from 1269 to 36 270 binary decision variables. Overall, the mean objective function values found with simulated annealing ranged from approximately 87% to 99% of the optima after 30 min of computing time, and a moderate downward trend of the relationship between problem size and solution quality was observed.

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.

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