A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence
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 Optimiza...
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/12/8/10990 |
| _version_ | 1798039788472762368 |
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| author | Vic Callaghan Marco Sotelo Efren Mezura Rosario Baltazar Leoncio A. Romero Victor Zamudio |
| author_facet | Vic Callaghan Marco Sotelo Efren Mezura Rosario Baltazar Leoncio A. Romero Victor Zamudio |
| author_sort | Vic Callaghan |
| collection | DOAJ |
| description | 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<em> Average Cumulative Oscillation</em> (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. |
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| format | Article |
| id | doaj.art-4cb7ffeee58d4b42909190406d2a6e93 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:58:32Z |
| publishDate | 2012-08-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-4cb7ffeee58d4b42909190406d2a6e932022-12-22T04:01:02ZengMDPI AGSensors1424-82202012-08-01128109901101210.3390/s120810990A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient IntelligenceVic CallaghanMarco SoteloEfren MezuraRosario BaltazarLeoncio A. RomeroVictor ZamudioIn 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<em> Average Cumulative Oscillation</em> (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.http://www.mdpi.com/1424-8220/12/8/10990cyclic instabilityambient intelligencelocking |
| spellingShingle | Vic Callaghan Marco Sotelo Efren Mezura Rosario Baltazar Leoncio A. Romero Victor Zamudio A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence Sensors cyclic instability ambient intelligence locking |
| title | A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence |
| title_full | A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence |
| title_fullStr | A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence |
| title_full_unstemmed | A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence |
| title_short | A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence |
| title_sort | comparison between metaheuristics as strategies for minimizing cyclic instability in ambient intelligence |
| topic | cyclic instability ambient intelligence locking |
| url | http://www.mdpi.com/1424-8220/12/8/10990 |
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