Identification of bioprocesses using random search and Simulated Annealing algorithms

2012 ◽  
Author(s):  
Lyes Saad Saoud ◽  
Faycal Rahmoune ◽  
Victor Tourtchine ◽  
Kamel Baddari
Keyword(s):  
Simulated Annealing ◽  
Random Search ◽  
Annealing Algorithms

scholarly journals Heterogeneous Simulated Annealing Teams: An Optimizing Search Algorithm Inspired by Engineering Design Teams

2018 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Simulated Annealing ◽  
Engineering Design ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Design Teams ◽  
Design Cognition ◽  
Gradient Based ◽  
Multi Agent ◽  
Annealing Algorithms

Although insights uncovered by design cognition are often utilized to develop the methods used by human designers, using such insights to inform computational methodologies also has the potential to improve the performance of design algorithms. This paper uses insights from research on design cognition and design teams to inform a better simulated annealing search algorithm. Simulated annealing has already been established as a model of individual problem solving. This paper introduces the Heterogeneous Simulated Annealing Team (HSAT) algorithm, a multi-agent simulated annealing algorithm. Each agent controls an adaptive annealing schedule, allowing the team develop heterogeneous search strategies. Such diversity is a natural part of engineering design, and boosts performance in other multi-agent algorithms. Further, interaction between agents in HSAT is structured to mimic interaction between members of a design team. Performance is compared to several other simulated annealing algorithms, a random search algorithm, and a gradient-based algorithm. Compared to other algorithms, the team-based HSAT algorithm returns better average results with lower variance.

scholarly journals Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2255
Author(s):  
Krzysztof Przystupa ◽  
Julia Pyrih ◽  
Mykola Beshley ◽  
Mykhailo Klymash ◽  
Andriy Branytskyy ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Random Search ◽  
Signal Propagation ◽  
Propagation Model ◽  
Route Search ◽  
Annealing Algorithms ◽  
Self Organizing Networks ◽  
Self Organizing

With the constant growth of requirements to the quality of infocommunication services, special attention is paid to the management of information transfer in wireless self-organizing networks. The clustering algorithm based on the Motley signal propagation model has been improved, resulting in cluster formation based on the criterion of shortest distance and maximum signal power value. It is shown that the use of the improved clustering algorithm compared to its classical version is more efficient for the route search process. Ant and simulated annealing algorithms are presented to perform route search in a wireless sensor network based on the value of the quality of service parameter. A comprehensive routing method based on finding the global extremum of an ordered random search with node addition/removal is proposed by using the presented ant and simulated annealing algorithms. It is shown that the integration of the proposed clustering and routing solutions can reduce the route search duration up to two times.

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

scholarly journals Comparison between simulated annealing algorithms and rapid chain delineation in the construction of genetic maps

2010 ◽  
Vol 33 (2) ◽  
pp. 398-408 ◽  
Author(s):  
Moysés Nascimento ◽  
Cosme Damião Cruz ◽  
Luiz Alexandre Peternelli ◽  
Ana Carolina Mota Campana
Keyword(s):  
Simulated Annealing ◽  
Genetic Maps ◽  
Annealing Algorithms
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