Dans ce travail de thèse, nous présentons une méthode originale s’inspirant des comportements des fourmis réelles pour la résolution de problème de classification non supervisée non hiérarchique. Cette approche créée dynamiquement des groupes de données. Elle est basée sur le concept des fourmis artificielles qui se déplacent en même temps de manière complexe avec les règles de localisation simples. Chaque fourmi représente une donnée dans l’algorithme. Les mouvements des fourmis visent à créer des groupes homogènes de données qui évoluent ensemble dans une structure de graphe. Nous proposons également une méthode de construction incrémentale de graphes de voisinage par des fourmis artificielles. Nous proposons deux méthodes qui se dérivent parmi les algorithmes biomimétiques. Ces méthodes sont hybrides dans le sens où la recherche du nombre de classes, de départ, est effectuée par l’algorithme de classification K-Means, qui est utilisé pour initialiser la première partition et la structure de graphe. / In this work, we present a novel method based on behavior of real ants for solving unsupervised non-hierarchical classification problem. This approach dynamically creates data groups. It is based on the concept of artificial ants moving complexly at the same time with simple location rules. Each ant represents a data in the algorithm. The movements of ants aim to create homogenous data groups that evolve together in a graph structure. We also propose a method of incremental building neighborhood graphs by artificial ants. We propose two approaches that are derived among biomimetic algorithms, they are hybrid in the sense that the search for the number of classes starting, which are performed by the classical algorithm K-Means classification, it is used to initialize the first partition and the graph structure.

Homeostatic self-regulation is a fundamental aspect of open dissipative systems. Integral feedback has been found to be important for homeostatic control on both the cellular and molecular levels of biological organization and in engineered systems. Analyzing the task allocation mechanisms of three insect societies, we identified a model of integral control residing at colony level. We characterized a general functional core mechanism, called the “common stomach,” where a crucial shared substance for colony function self-regulates its own quantity via reallocating the colony’s workforce, which collects and uses this substance. The central component in a redundant feedback network is the saturation level of this substance in the colony. An interaction network of positive and negative feedback loops ensures the homeostatic state of this substance and the workforce involved in processing this substance. Extensive sensitivity and stability analyses of the core model revealed that the system is very resilient against perturbations and compensates for specific types of stress that real colonies face in their ecosystems. The core regulation system is highly scalable, and due to its buffer function, it can filter noise and find a new equilibrium quickly after environmental (supply) or colony-state (demand) changes. The common stomach regulation system is an example of convergent evolution among the three different societies, and we predict that similar integral control regulation mechanisms have evolved frequently within natural complex systems.

This thesis presents work based on modeling multi-agent coalition formation in an agent organized network. Agents choose which agents to connect with in the network. Tasks are periodically introduced into the network. Each task is defined by a set of skills that agents must fill. Agents form a coalition to complete a task by either joining an existing coalition a network neighbor belongs to, or by proposing a new coalition for a task no agents have proposed a coalition for. We introduce task patience and strategic task selection and show that they improve the number of successful coalitions agents form. We also introduce new methods of choosing agents to connect to in the network and compare the performance of these and existing methods.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis discusses about developing a routing protocol of mobile ad hoc networks in a bio inspired manner. Algorithms inspired by collective behaviour of social insect colonies, bird flocking, honey bee dancing, etc., promises to be capable of catering to the challenges faced by tiny wireless sensor networks. Challenges include but are not limited to low bandwidth, low memory, limited battery life, etc. This thesis proposes an energy efficient multi-path routing algorithm based on foraging nature of ant colonies and considers many other meta-heuristic factors to provide good robust paths from source node to destination node in a hope to overcome the challenges posed by resource constrained sensors. / 2020-12-31

This thesis deals with robot path planning. It contains an overview of general approaches for path planning and describes methods of swarm intelligence and their application for robot path planning. This paper also contains proposals of adjustments for ant algorithms and it presents experimental results of algorithm implementation.

No description available.

A swarm is a group of uninformed individuals that exhibit collective behaviors. Without any information about the external world, a swarm has limited ability to achieve complex goals. Prior work on human-swarm interaction methods allow a human to influence these uninformed individuals through either leadership or predation as informed agents that directly interact with humans. These methods of influence have two main limitations: (1) although leaders sustain influence over nominal agents for a long period of time, they tend to cause all collective structures to turn in to flocks (negating the benefit of other swarm formations) and (2) predators tend to cause collective structures to fragment. In this thesis, we present the use of mediators as a novel form for human-swarm influence and use mediators to shape the perimeter of a swarm. The mediator method uses special agents that operate from within the spatial center of a swarm. This approach allows a human operator to coordinate multiple mediators to modulate a rotating torus into various shapes while sustaining influence over the swarm, avoiding fragmentation, and maintaining the swarm's connectivity. The use of mediators allows a human to mold and adapt the torus' behavior and structure to a wide range of spatio-temporal tasks such as military protection and decontamination tasks. Results from an experiment that compares previous forms of human influence with mediator-based control indicate that mediator-based control is more amenable to human influence for certain types of problems.

Yes / The Sustainable Development Goals (SDGs) present the emerging need to explore new ways of AgriFood production and food security as ultimate targets for feeding future generations. The study adopts a Design Science methodology and proposes Artificial Intelligence (AI) techniques as a solution to food security problems. Specifically, the proposed artefact presents the collective use of Agricultural Technology (AgriTech) drones inspired by the biomimetic ways of bird swarms. The design (artefact) appears here as a solution for supporting farming operations in inaccessible land, so as unmanned aerial devices contribute and improve the productivity of farming areas with limited capacity. The proposed design is developed through a scenario of drone swarms applying AI techniques to address food security issues. The study concludes by presenting a research agenda and the sectoral challenges triggered by the applications of AI in Agriculture. / European Union's H2020 research and innovation programme under the Marie Skłodowska-Curie grant (agreement No. 746667)

Networks that self-organize in response to information are one of the most central studies in complex systems theory. A new time series analysis tool for studying self-organizing systems is developed and demonstrated. This method is applied to interacting complex swarms to explore the connection between information transport and group size, providing evidence for Dunbar's numbers having a foundation in network dynamics. A complex network model of information spread is developed. This network infodemic model uses reinforcement learning to simulate connection and opinion adaptation resulting from interaction between units. The model is applied to study polarized populations and echo chamber formation, exploring strategies for network resilience and weakening. The model is straightforward to extend to multilayer networks and networks generated from real world data. By unifying explanation and prediction, the network infodemic model offers a timely step toward understanding global collective behavior.

<p>Network and system management has always been of concern for telecommunication and computer system operators. The need for standardization was recognised already 20 years ago, hence several standards for network management exist today. However, the ever-increasing number of units connected to networks and the ever-increasing number of services being provided results in significant increased complexity of average network environments. This challenges current management systems. In addition to the general increase in complexity the trend among network owners and operators of merging several single service networks into larger, heterogeneous and complex full service networks challenges current management systems even further. The full service networks will require management systems more powerful than what is possible to realize basing systems purely on todays management standards. This thesis presents a distributed stochastic optimization algorithm which enables implementations of highly robust and efficient management tools. These tools may be integrated into management systems and potentially make the systems more powerful and better prepared for management of full service networks.</p><p>Emergent behavior is common in nature and easily observable in colonies of social insects and animals. Even an old oak tree can be viewed as an emergent system with its collection of interacting cells. Characteristic for any emergent system is how the overall behavior of the system emerge from many relatively simple, restricted behaviors interacting, e.g. a thousand ants building a trail, a flock of birds flying south or millions of cells making a tree grow. No centralized control exist, i.e. no single unit is in charge making global decisions. Despite distributed control, high work redundancy and stochastic behavior components, emergent systems tend to be very efficient problem solvers. In fact emergent systems tend to be both efficient, adaptive and robust which are three properties indeed desirable for a network management system. The algorithm presented in this thesis relates to a class of emergent behavior based systems known as swarm intelligence systems, i.e. the algorithm is potentially efficient, adaptive and robust.</p><p>On the contrary to other related swarm intelligence algorithms, the algorithm presented has a thorough formal foundation. This enables a better understanding of the algorithm’s potentials and limitations, and hence enables better adaptation of the algorithm to new problem areas without loss of efficiency, adaptability or robustness. The formal foundations are based on work by Reuven Rubinstein on cross entropy driven optimization. The transition from Ruinstein’s centralized and synchronous algorithm to a distributed and asynchronous algorithm is described, and the distributed algorithm’s ability to solve complex problems (NP-complete) efficiently is demonstrated.</p><p>Four examples of how the distributed algorithm may be applied in a network management context are presented. A system for finding near optimal patterns of primary/backup paths together with a system for finding cyclic protection paths in mesh networks demonstrate the algorithm’s ability to act as a tool helping management system to ensure quality of service. The algorithm’s potential as a management policy implementation mechanism is also demonstrated. The algorithm’s adaptability is shown to enable resolution of policy conflicts in a soft manner causing as little loss as possible. Finally, the algorithm’s ability to find near optimal paths (i.e. sequences) of resources in networks of large scale is demonstrated.</p>