A continuous model of ant foraging with pheromones and trail formation

We propose and numerically analyze a PDE model of ant foraging behavior. Ant foraging is a prime example of individuals following simple behavioral rules based on local information producing complex, organized and ``intelligent'' strategies at the population level. One of its main aspects is the widespread use of pheromones, which are chemical compounds laid by the ants used to attract other ants to a food source. In this work, we consider a continuous description of a population of ants and simulate numerically the foraging behavior using a system of PDEs of chemotaxis type. We show that, numerically, this system accurately reproduces observed foraging behavior, such as trail formation and efficient removal of food sources.Comment: Conference proceeding

Deconstructing collective building in social insects : implications for ecological adaptation and evolution

Funding: John Templeton Foundation as part of the research collaboration grant “Putting the extended evolutionary synthesis to the test” (Grant no. 60501).Nests built by eusocial insect species are often complex structures consisting of multiple effectively integrated and functionally distinct substructures. Stigmergy, self-assembly and self-organisation have been proposed as the mechanisms that translate simple individual behaviour into coordinated collective activity. Here, we consider these processes focusing on their implications for the generation of new structures, nest adaptiveness and the evolution of building rules. We discuss in particular how self-organisation and stigmergy may guide the shift between substructures during building and generate new elements, either as an indirect result of building rule sets evolved for other purposes and under direct selection. The same mechanisms generate local, short-term adaptation through exploration of the phenotype space of the construction. Finally, we introduce the hypothesis that feedback dynamics create evolutionary transition between collective level phenotypes when mutations arise in the worker line, thus facilitating colony survival and affecting the evolution of collective building rules and of nest shape. This smooth transition is possible only when the new and the old rule variant are compatible. We call for new research that investigates self-organisation in collective building from an evolutionary perspective.Publisher PDFPeer reviewe

Understanding how Knowledge is exploited in Ant Algorithms

Centre for Intelligent Systems and their ApplicationsAnt algorithms were first written about in 1991 and since then they have been applied to many problems with great success. During these years the algorithms themselves have been modified for improved performance and also been influenced by research in other fields. Since the earliest Ant algorithms, heuristics and local search have been the primary knowledge sources. This thesis asks the question "how is knowledge used in Ant algorithms?" To answer this question three Ant algorithms are implemented. The first is the Graph based Ant System (GBAS), a theoretical model not yet implemented, and the others are two influential algorithms, the Ant System and Max-Min Ant System. A comparison is undertaken to show that the theoretical model empirically models what happens in the other two algorithms. Therefore, this chapter explores whether different pheromone matrices (representing the internal knowledge) have a significant effect on the behaviour of the algorithm. It is shown that only under extreme parameter settings does the behaviour of Ant System and Max-Min Ant System differ from that of GBAS. The thesis continues by investigating how inaccurate knowledge is used when it is the heuristic that is at fault. This study reveals that Ant algorithms are not good at dealing with this information, and if they do use a heuristic they must rely on it relating valid guidance. An additional benefit of this study is that it shows heuristics may offer more control over the exploration-exploitation trade-off than is afforded by other parameters. The second point where knowledge enters the algorithm is through the local search. The thesis looks at what happens to the performance of the Ant algorithms when a local search is used and how this affects the parameters of the algorithm. It is shown that the addition of a local search method does change the behaviour of the algorithm and that the strength of the method has a strong influence on how the parameters are chosen. The final study focuses on whether Ant algorithms are effective for driving a local search method. The thesis demonstrates that these algorithms are not as effective as some simpler fixed and variable neighbourhood search methods

Modeling ant behavior under a variable environment

International audienceThis paper studies the behavior of ants when moving in an artificial network composed of several interconnected paths linking their nest to a food source. The ant responses when temporarily blocking the access to some branches of the maze were observed in order to study which factors influenced their local decisions about the paths to follow. We present a mathematical model based on experimental observations that simulates the motion of ants through the network. In this model, ants communicate through the deposition of a trail pheromone that attracts other ants. In addition to the trail laying/following process, several other aspects of ant behavior were modeled. The paths selected by ants in the simulations were compared to those selected by ants in the experiments. The results of the model were encouraging, indicating that the same behavioral rules can lead ants to find the shortest paths under different environmental conditions