RBF Neural Network based on Global Optimum Artificial Fish Swarm Algorithm
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The swarm behaviour can be fully determined by attractants (food pieces) which change the directions of swarm propagation. If we assume that at each time step the swarm can find out not more than p – 1 attractants, then the swarm behaviour can be coded by p-adic integers. The main task of any swarm is to logistically optimize the road system connecting the reachable attractants. In the meanwhile, the transporting network of the swarm has loops (circles) and permanently changes, e.g. the swarm occupies some attractants and leaves the others. However, this complex dynamics can be effectively coded by p-adic integers. This allows us to represent the swarm behaviour as a calculation on p-adic valued strings.
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(Robot) swarm networks are consisting of magnitudes of individual agents that are capable to move in a world and interact with each other or with passive items. Through local, individual algorithms applied in the agents desired properties of the swarm can emerge. In this paper, we present Swarm-Sim, a round-based simulator that supports the evaluation of such large scaled swarms in a 2D and 3D world. Agents can move in the simulated world, perceive their surroundings, carry other agents and items, as well as communicate with the position they stand on through pheromones or markings as well as with other agents and items in this world. Through this, many swarm-related use cases are supported. Implementing swarm algorithms is easy as the simulator is completely written in python and its code is open source. This allows also to simulate large swarms due to the good performance of the simulator. Thorough analysis of the swarm behavior is supported through plots and visualizations.
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An M-Member swarm system with energetic behavior is studied in this paper. A new type of swarm controller is developed such that a swarm can follow a desired trajectory with different swarm temperatures and potential energy values. The temperature allows the internal kinetic energy of the swarm to be modulated. As the temperature increases the motion of the swarm becomes more energetic and areas are covered by the swarm in less time. The potential energy controls the size of the swarm and also provides new guarantees of energetic swarm cohesion. Simulation is used to validate the results and to demonstrate the new approach.
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This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task.
The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.
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The nationally-recognized Susquehanna
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This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic policy evolution to provide agility within the swarm, enabling each swarm member to select the most appropriate mobility policy for the environment given the swarm's abilities. The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. These methods are either human-designed, which the swarm adopts when most appropriate, or are novel hybridisations that the swarm creates for the problem. This paper also tests the swarm with individuals being removed during deployment. It is found that the swarm is immune to most alterations, though removal of specialised members of the heterogeneous swarm leads to temporary failure. The swarm evolution can then correct this failure by adjusting the swarm behaviour.
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This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task.
The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.
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