Research
In conclusion I have proposed an innovative way of modeling that is completly deterministic involving nonlinear decisional processes into the swarm intelligence field.
!!The Logistic Multi-Agent System
Some slides summarizing this model (in french):[[(Attach:)Thesis_slides.pdf]]
Research
Research.Research History
Hide minor edits - Show changes to output
Changed lines 41-43 from:
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting metaheuristics. Without being more powerful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising and comparable results to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also brings an new reading of the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
In conclusion I have proposed an innovativeway of modeling that is completly deterministic involving nonlinear decisional processes into the swarm intelligence field.
In conclusion I have proposed an innovative
to:
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting metaheuristics. Without being more powerful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising results, comparable to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also sheds a new light on the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
In conclusion I have proposed an innovative modeling approach that is completely deterministic involving nonlinear decisional processes into Swarm Intelligence.
In conclusion I have proposed an innovative modeling approach that is completely deterministic involving nonlinear decisional processes into Swarm Intelligence.
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The interest and the novelty of the logistic multi-agent system lies in its general framework to model distinct phenomena such as collective motion (flocking behaviors) or stigmergic mechanisms based on pheromone deposits (in particular ant forraging).
to:
The interest and the novelty of the logistic multi-agent system lies in its general framework to model distinct phenomena such as collective motion (flocking behaviors) or stigmergic mechanisms based on pheromone deposits (in particular ant foraging).
Changed lines 36-41 from:
* It gives computational quantities to follow the dynamics such as Lyapunov coefficient or Kolmogorov entropy. These quantities may be aproximated as the work on flocking simulations have shown.
* It can generate stochasticbehavior without probabilities, which is needed for the exploration phases for optimization problems, by means of chaotic dynamics.
* The deterministic aspect of the dynamics enables to monitor the behavior of the system to a given extent and to predictqualitatively its future.
The logistic multi-agent system is therefore a new approach in the Swarm Intelligence field since all the existing algorithms are based on stochastic approaches.
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect ofthe logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting ones. Without being more powerful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising and comparable results to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also brings an new reading of the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
* It can generate stochastic
* The deterministic aspect of the dynamics enables to monitor the behavior of the system to a given extent and to predict
The
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of
to:
* It provides computational quantities to follow the dynamics such as Lyapunov coefficient or Kolmogorov entropy. These quantities may be aproximated as the work on flocking simulations have shown.
* It can generate stochastic behaviors without probabilities, which is needed for the exploration phases for optimization problems, by means of chaotic dynamics.
* The deterministic aspect of the dynamics enables to monitor the behavior of the system to a given extent and to predict its future qualitatively.
The logistic multi-agent system is therefore a new approach in the Swarm Intelligence field since all other existing algorithms --to our knowledge-- are based on stochastic approaches.
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting metaheuristics. Without being more powerful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising and comparable results to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also brings an new reading of the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
* It can generate stochastic behaviors without probabilities, which is needed for the exploration phases for optimization problems, by means of chaotic dynamics.
* The deterministic aspect of the dynamics enables to monitor the behavior of the system to a given extent and to predict its future qualitatively.
The logistic multi-agent system is therefore a new approach in the Swarm Intelligence field since all other existing algorithms --to our knowledge-- are based on stochastic approaches.
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting metaheuristics. Without being more powerful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising and comparable results to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also brings an new reading of the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
Changed lines 3-4 from:
My research topics belong to the field of Swarm Intelligence and Complex Systems, adressing both theoretical and applied aspects :
to:
My research topics belong to the field of Swarm Intelligence and Complex Systems, addressing both theoretical and applied aspects :
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!! Abstract of my thesis
to:
!! Abstract of my PhD thesis
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Its associated problematics meet many other fields and scientific questions. The concept of swarm in particular belongs to the science called the science of complex systems. This phd thesis shows the design and the characteristics and the applications of a novel type of model called the logistic multi-agent system (LMAS) dedicated to the Swarm Intelligence field.
The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such as synchronization and parametric control which are considered as the main mechanisms of self-organization and adaptation in the heart of the system.
The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such
to:
Its associated problematics meet many other fields and scientific questions. The concept of swarm in particular belongs to the science called the science of complex systems. This PhD thesis shows the design and characteristics of a novel type of model called the logistic multi-agent system (LMAS) dedicated to Swarm Intelligence.
The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such as synchronization and parametric control which are considered as the main mechanisms of self-organization and adaptation at the heart of the system.
The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such as synchronization and parametric control which are considered as the main mechanisms of self-organization and adaptation at the heart of the system.
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The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for the Swarm Intelligence.
to:
The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for Swarm Intelligence.
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In conclusion I have proposed an innovative way of modeling that is completly deterministic involving nonlinear decisional processes into the swarm intelligence field.
Changed lines 26-27 from:
Some slides summarizing this model (in french):[[(Attach:)Thesis_slides.pdf]]
to:
Some slides summarizing this model: \\
(in french):[[(Attach:)Thesis_slides.pdf]]
(in french):[[(Attach:)Thesis_slides.pdf]]
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!!The Logistic Multi-Agent System
Some slides summarizing this model (in french):[[(Attach:)Thesis_slides.pdf]]
Changed line 37 from:
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting ones. Without being more successful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising and comparable results to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also brings an new reading of the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
to:
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting ones. Without being more powerful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising and comparable results to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also brings an new reading of the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
Added line 37:
Finally in terms of applications to classical problems in computer science, the metaheuristic aspect of the logistic multi-agent system (LMAS) for optimization has been tested and compared to exiting ones. Without being more successful at this level of development, the LMAS - renamed in this context as the algorithm of logistic ants – obtains very promising and comparable results to the first versions of ACO (Ant Colony Optimization) on small TSP instances. Due to its determinist characteristics it also brings an new reading of the involved mechanisms. The convergence principle of the algorithm is rather similar to the well-known simulated annealing algorithm.
Changed lines 25-36 from:
!!Summary of my results
to:
!!Summary of my contributions
The interest and the novelty of the logistic multi-agent system lies in its general framework to model distinct phenomena such as collective motion (flocking behaviors) or stigmergic mechanisms based on pheromone deposits (in particular ant forraging).
This model leads moreover to explain different types of behaviors by means of dynamical analysis according to a mechanist philosophy. We have shown in that way that :
* Collective motion as an instance of self-organization is caused by internal synchronization between the agent states.
* Stigmergic mechanisms are achieved by a decentralized control within agents which is governed by the amount of pheromone perceived in the environment.
The deterministic dynamical system theory which the logistic multi-agent system is based on, has some advantages:
* It gives computational quantities to follow the dynamics such as Lyapunov coefficient or Kolmogorov entropy. These quantities may be aproximated as the work on flocking simulations have shown.
* It can generate stochastic behavior without probabilities, which is needed for the exploration phases for optimization problems, by means of chaotic dynamics.
* The deterministic aspect of the dynamics enables to monitor the behavior of the system to a given extent and to predict qualitatively its future.
The logistic multi-agent system is therefore a new approach in the Swarm Intelligence field since all the existing algorithms are based on stochastic approaches.
The interest and the novelty of the logistic multi-agent system lies in its general framework to model distinct phenomena such as collective motion (flocking behaviors) or stigmergic mechanisms based on pheromone deposits (in particular ant forraging).
This model leads moreover to explain different types of behaviors by means of dynamical analysis according to a mechanist philosophy. We have shown in that way that :
* Collective motion as an instance of self-organization is caused by internal synchronization between the agent states.
* Stigmergic mechanisms are achieved by a decentralized control within agents which is governed by the amount of pheromone perceived in the environment.
The deterministic dynamical system theory which the logistic multi-agent system is based on, has some advantages:
* It gives computational quantities to follow the dynamics such as Lyapunov coefficient or Kolmogorov entropy. These quantities may be aproximated as the work on flocking simulations have shown.
* It can generate stochastic behavior without probabilities, which is needed for the exploration phases for optimization problems, by means of chaotic dynamics.
* The deterministic aspect of the dynamics enables to monitor the behavior of the system to a given extent and to predict qualitatively its future.
The logistic multi-agent system is therefore a new approach in the Swarm Intelligence field since all the existing algorithms are based on stochastic approaches.
Changed lines 3-4 from:
My research topics belong to the field of Swarm Intelligence and Complex Systems, both in theoretical and applied aspects :
to:
My research topics belong to the field of Swarm Intelligence and Complex Systems, adressing both theoretical and applied aspects :
Changed lines 23-25 from:
The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for the Swarm Intelligence.
to:
The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for the Swarm Intelligence.
!!Summary of my results
!!Summary of my results
Added lines 1-23:
!! Main topics
My research topics belong to the field of Swarm Intelligence and Complex Systems, both in theoretical and applied aspects :
* Synthesis of Artificial Complex Systems
* Collective motion mechanisms
* Self-organization by synchronization mechanisms
* Adaptation by control mechanisms
* Nonlinear dynamics
Application domains :
* Flocking simulations
* Flocks in Robotics
* New metaheuristics for optimization
!! Abstract of my thesis
Swarm Intelligence is from now on a full part of Distributed Artificial Intelligence.
Its associated problematics meet many other fields and scientific questions. The concept of swarm in particular belongs to the science called the science of complex systems. This phd thesis shows the design and the characteristics and the applications of a novel type of model called the logistic multi-agent system (LMAS) dedicated to the Swarm Intelligence field.
The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such as synchronization and parametric control which are considered as the main mechanisms of self-organization and adaptation in the heart of the system.
The field-layered based environment is the other important feature of the LMAS, since it enables indirect interactions and plays the part of a data structure for the whole system. The work of this thesis is put into practice for simulation and optimization.
The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for the Swarm Intelligence.
My research topics belong to the field of Swarm Intelligence and Complex Systems, both in theoretical and applied aspects :
* Synthesis of Artificial Complex Systems
* Collective motion mechanisms
* Self-organization by synchronization mechanisms
* Adaptation by control mechanisms
* Nonlinear dynamics
Application domains :
* Flocking simulations
* Flocks in Robotics
* New metaheuristics for optimization
!! Abstract of my thesis
Swarm Intelligence is from now on a full part of Distributed Artificial Intelligence.
Its associated problematics meet many other fields and scientific questions. The concept of swarm in particular belongs to the science called the science of complex systems. This phd thesis shows the design and the characteristics and the applications of a novel type of model called the logistic multi-agent system (LMAS) dedicated to the Swarm Intelligence field.
The LMAS has its foundations in complex system modeling: it is inspired from the coupled logistic map lattice model which has been adapted to the ``Influence-Reaction'' modeling of multi-agent systems. This model is based on universal principles such as synchronization and parametric control which are considered as the main mechanisms of self-organization and adaptation in the heart of the system.
The field-layered based environment is the other important feature of the LMAS, since it enables indirect interactions and plays the part of a data structure for the whole system. The work of this thesis is put into practice for simulation and optimization.
The novelty of the LMAS lies in its generic theoretical framework, which enables to tackle problems considered as distinct in the literature, in particular flocking and ant-like stigmergic behavior. This model meets the need of explaining basic mechanisms and the need of synthesizing generative algorithms for the Swarm Intelligence.