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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.


Behavior of the instance 2 (Non-oscillating).
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f8-sensors-12-10990: Behavior of the instance 2 (Non-oscillating).

Mentions: In the case of instance 2 (Non-oscillating) shown in Figure 8 although the system does not oscillate the techniques were able to find different configurations for this scenario that remain stable while decrease the value of the Average Cumulative Oscillation. The behavior of instance 2 (non-oscillating) after locking is shown in Figure 9.


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)

Behavior of the instance 2 (Non-oscillating).
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-12-10990: Behavior of the instance 2 (Non-oscillating).
Mentions: In the case of instance 2 (Non-oscillating) shown in Figure 8 although the system does not oscillate the techniques were able to find different configurations for this scenario that remain stable while decrease the value of the Average Cumulative Oscillation. The behavior of instance 2 (non-oscillating) after locking is shown in Figure 9.

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.