Biomedical subjects

Marco Dorigo

Publications and source records attributed to Marco Dorigo.

4 recordsLinked to original sources

PubMed2006

Self-organisation and communication in groups of simulated and physical robots.

In social insects, both self-organisation and communication play a crucial role for the accomplishment of many tasks at a collective level. Communication is performed with different modalities, which can be roughly classified into three classes: indirect (stigmergic) communication, direct interactions and direct communication. The use of stigmergic communication is predominant in social insects (e.g. the pheromone trails in ants), where, however, direct interactions (e.g. antennation in ants) and direct communication (e.g. the waggle dance in honey bees) can also be observed. Taking inspiration from insect societies, we present an experimental study of self-organising behaviours for a group of robots, which exploit communication to coordinate their activities. In particular, the robots are placed in an arena presenting holes and open borders, which they should avoid while moving coordinately. Artificial evolution is responsible for the synthesis in a simulated environment of the robot's neural controllers, which are subsequently tested on physical robots. We study different communication strategies among the robots: no direct communication, handcrafted signalling and a completely evolved approach. We show that the latter is the most efficient, suggesting that artificial evolution can produce behaviours that are more adaptive than those obtained with conventional design methodologies. Moreover, we show that the evolved controllers produce a self-organising system that is robust enough to be tested on physical robots, notwithstanding the huge gap between simulation and reality.

Algorithms↗

PubMed2004

The hyper-cube framework for ant colony optimization.

Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in Ant System, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.

Algorithms↗

PubMed2003

Development of a novel titration and off-gas analysis (TOGA) sensor for study of biological processes in wastewater treatment systems.

The development of the new TOGA (titration and off-gas analysis) sensor for the detailed study of biological processes in wastewater treatment systems is outlined. The main innovation of the sensor is the amalgamation of titrimetric and off-gas measurement techniques. The resulting measured signals are: hydrogen ion production rate (HPR), oxygen transfer rate (OTR), nitrogen transfer rate (NTR), and carbon dioxide transfer rate (CTR). While OTR and NTR are applicable to aerobic and anoxic conditions, respectively, HPR and CTR are useful signals under all of the conditions found in biological wastewater treatment systems, namely, aerobic, anoxic and anaerobic. The sensor is therefore a powerful tool for studying the key biological processes under all these conditions. A major benefit from the integration of the titrimetric and off-gas analysis methods is that the acid/base buffering systems, in particular the bicarbonate system, are properly accounted for. Experimental data resulting from the TOGA sensor in aerobic, anoxic, and anaerobic conditions demonstrates the strength of the new sensor. In the aerobic environment, carbon oxidation (using acetate as an example carbon source) and nitrification are studied. Both the carbon and ammonia removal rates measured by the sensor compare very well with those obtained from off-line chemical analysis. Further, the aerobic acetate removal process is examined at a fundamental level using the metabolic pathway and stoichiometry established in the literature, whereby the rate of formation of storage products is identified. Under anoxic conditions, the denitrification process is monitored and, again, the measured rate of nitrogen gas transfer (NTR) matches well with the removal of the oxidised nitrogen compounds (measured chemically). In the anaerobic environment, the enhanced biological phosphorus process was investigated. In this case, the measured sensor signals (HPR and CTR) resulting from acetate uptake were used to determine the ratio of the rates of carbon dioxide production by competing groups of microorganisms, which consequently is a measure of the activity of these organisms. The sensor involves the use of expensive equipment such as a mass spectrometer and requires special gases to operate, thus incurring significant capital and operational costs. This makes the sensor more an advanced laboratory tool than an on-line sensor.

Bacteria, Aerobic↗

PubMed2002

Ant colony optimization and stochastic gradient descent.

In this article, we study the relationship between the two techniques known as ant colony optimization (ACO) and stochastic gradient descent. More precisely, we show that some empirical ACO algorithms approximate stochastic gradient descent in the space of pheromones, and we propose an implementation of stochastic gradient descent that belongs to the family of ACO algorithms. We then use this insight to explore the mutual contributions of the two techniques.

Algorithms↗