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A comparison between metaheuristics as strategies for minimizing cyclic instability in Ambient Intelligence.

Romero LA, Zamudio V, Baltazar R, Mezura E, Sotelo M, Callaghan V - Sensors (Basel) (2012)

Bottom Line: It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked.These results were confirmed using the Wilcoxon Signed Rank Test.This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

View Article: PubMed Central - PubMed

Affiliation: Division of Research and Postgraduate Studies, Leon Institute of Technology, Leon, Guanajuato 37290, Mexico. leoncior@acm.org

ABSTRACT
In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

No MeSH data available.


Instabilities are successfully removed for the instance 3 (Toad) using all algorithms.
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f11-sensors-12-10990: Instabilities are successfully removed for the instance 3 (Toad) using all algorithms.

Mentions: In the same way as in instance 1(Blinker), instability was eliminated successfully for instance 3 (Toad) as shown in Figure 10. As shown in Tables 7 and 8, different values of the ACO were obtained for this scenario because there are different vectors of locked agents that allow to stabilize the system. This explains why different system evolution is shown in Figure 11 after applying the locking.


A comparison between metaheuristics as strategies for minimizing cyclic instability in Ambient Intelligence.

Romero LA, Zamudio V, Baltazar R, Mezura E, Sotelo M, Callaghan V - Sensors (Basel) (2012)

Instabilities are successfully removed for the instance 3 (Toad) using all algorithms.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472871&req=5

f11-sensors-12-10990: Instabilities are successfully removed for the instance 3 (Toad) using all algorithms.
Mentions: In the same way as in instance 1(Blinker), instability was eliminated successfully for instance 3 (Toad) as shown in Figure 10. As shown in Tables 7 and 8, different values of the ACO were obtained for this scenario because there are different vectors of locked agents that allow to stabilize the system. This explains why different system evolution is shown in Figure 11 after applying the locking.

Bottom Line: It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked.These results were confirmed using the Wilcoxon Signed Rank Test.This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

View Article: PubMed Central - PubMed

Affiliation: Division of Research and Postgraduate Studies, Leon Institute of Technology, Leon, Guanajuato 37290, Mexico. leoncior@acm.org

ABSTRACT
In this paper we present a comparison between six novel approaches to the fundamental problem of cyclic instability in Ambient Intelligence. These approaches are based on different optimization algorithms, Particle Swarm Optimization (PSO), Bee Swarm Optimization (BSO), micro Particle Swarm Optimization (μ-PSO), Artificial Immune System (AIS), Genetic Algorithm (GA) and Mutual Information Maximization for Input Clustering (MIMIC). In order to be able to use these algorithms, we introduced the concept of Average Cumulative Oscillation (ACO), which enabled us to measure the average behavior of the system. This approach has the advantage that it does not need to analyze the topological properties of the system, in particular the loops, which can be computationally expensive. In order to test these algorithms we used the well-known discrete system called the Game of Life for 9, 25, 49 and 289 agents. It was found that PSO and μ-PSO have the best performance in terms of the number of agents locked. These results were confirmed using the Wilcoxon Signed Rank Test. This novel and successful approach is very promising and can be used to remove instabilities in real scenarios with a large number of agents (including nomadic agents) and complex interactions and dependencies among them.

No MeSH data available.