Add to ORKGBased on the microcanonical Monte Carlo method of statistical physics, we present a new deterministic algorithm for simulated annealing - Microcanonical Annealing (MA). It is shown to be a metaheuristic algorithm for the analysis of a variety of problems involving combinatorial optimization. Its performance has been evaluated by introducing the traveling salesman problem. When compared to traditional simulated annealing (SA) method, MA has yielded the best overall results - the new deterministic method generates optimal solutions much faster and better than SA.BiologyComputer Science, Information SystemsEngineering, EnvironmentalEngineering, CivilEngineering, MechanicalMaterials Science, MultidisciplinaryCPCI-S(ISTP)
[[abstract]]Complex optimisation problems with many degrees of freedom are often characterised by th...
[[abstract]]A scalable linear algorithm of simulated annealing (SA) was proposed by Lin et al. that ...
Advanced optimization techniques, based on analogies related to physical systems rather than on clas...
Simulated annealing and related Monte Carlo-type optimization algorithms are used to apply statistic...
We introduce four new general optimization algorithms based on the 'demon' algorithm from statistica...
We introduce four new general optimization algorithms based on the `demon' algorithm from stati...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
Simulated annealing is a general approach for approximately solving large combinatorial optimization...
The analogy between combinatorial optimization and statistical mechanics has proven to be a fruitfu...
The goal of the research out of which this monograph grew, was to make annealing as much as possible...
All topics in this dissertation are centered around global optimization problems. The major part of ...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
The essence of metaheuristic methods and conditions of their application are considered, in particul...
Many problems in engineering, planning and manufacturing can be modeled as that of minimizing or max...
[[abstract]]Complex optimisation problems with many degrees of freedom are often characterised by th...
[[abstract]]A scalable linear algorithm of simulated annealing (SA) was proposed by Lin et al. that ...
Advanced optimization techniques, based on analogies related to physical systems rather than on clas...
Simulated annealing and related Monte Carlo-type optimization algorithms are used to apply statistic...
We introduce four new general optimization algorithms based on the 'demon' algorithm from statistica...
We introduce four new general optimization algorithms based on the `demon' algorithm from stati...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
Simulated annealing is a general approach for approximately solving large combinatorial optimization...
The analogy between combinatorial optimization and statistical mechanics has proven to be a fruitfu...
The goal of the research out of which this monograph grew, was to make annealing as much as possible...
All topics in this dissertation are centered around global optimization problems. The major part of ...
Simulated annealing is a combinatorial optimization method based on randomization techniques. The me...
The essence of metaheuristic methods and conditions of their application are considered, in particul...
Many problems in engineering, planning and manufacturing can be modeled as that of minimizing or max...
[[abstract]]Complex optimisation problems with many degrees of freedom are often characterised by th...
[[abstract]]A scalable linear algorithm of simulated annealing (SA) was proposed by Lin et al. that ...
Advanced optimization techniques, based on analogies related to physical systems rather than on clas...