Next Article in Journal
Fully Integrated Biochip Platforms for Advanced Healthcare
Previous Article in Journal
Use of a Combined SpO2/PtcCO2 Sensor in the Delivery Room
Article Menu

Export Article

Open AccessArticle
Sensors 2012, 12(8), 10990-11012;

A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence

Division of Research and Postgraduate Studies, Leon Institute of Technology, Leon, Guanajuato 37290, Mexico
Laboratorio Nacional de Informatica Avanzada, Xalapa, Veracruz 91000, Mexico
School of Computer Science and Electronic Engineering, University of Essex,Wivenhoe Park CO4 3SQ, UK
Author to whom correspondence should be addressed.
Received: 3 June 2012 / Revised: 3 July 2012 / Accepted: 10 July 2012 / Published: 8 August 2012
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [1296 KB, uploaded 21 June 2014]


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. View Full-Text
Keywords: cyclic instability; ambient intelligence; locking cyclic instability; ambient intelligence; locking
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Romero, L.A.; Zamudio, V.; Baltazar, R.; Mezura, E.; Sotelo, M.; Callaghan, V. A Comparison between Metaheuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence. Sensors 2012, 12, 10990-11012.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top