Browsing by Subject "Swarm intelligence."
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Item Emergent behaviors of multi-objective swarms with applications in a dynamic underwater environment.(2013-09-24) Roach, Jon H.; Marks, Robert J.; Electrical and Computer Engineering.; The Applied Research Lab at the Pennsylvania State University.; Baylor University. Dept. of Electrical and Computer Engineering.The allocation of resources between tasks within a swarm of agents can be difficult without a centralized controller. This problem is prevalent when designing a swarm of Autonomous Underwater Vehicles, in which underwater communication becomes challenging and a centralized controller cannot be used. In this thesis, a disjunctive fuzzy control system is used to solve the problem of resource management. Multi-objective, multi-state swarms are evolved with an offline learning algorithm to adapt to dynamic scenarios. Some of the emergent behaviors developed through the evolutionary algorithm are state-switching and recruitment techniques. In addition, the adaptability of swarms is tested by removing sensors from the system and re-evolving the swarm to allow it to compensate for its sensor loss. The concepts of a multi-objective, multi-state swarm are also applied to an underwater minefield mapping scenario, which is used to test the robustness of the swarm with respect to swarm size.Item Image compression and recovery using compressive sampling and particle swarm optimization.(2009-08-25T16:32:33Z) Van Ruitenbeek, Benjamin D.; Sturgill, David Brian.; Computer Science.; Baylor University. Dept. of Computer Science.We present a novel method for sparse signal recovery using Particle Swarm Optimization and demonstrate an application in image compression. Images are compressed with compressive sampling, and then reconstructed with particle swarm techniques. Several enhancements to the basic particle swarm algorithm are shown to improve signal recovery accuracy. We also present techniques specifically for reconstructing sparse image data and evaluate their performance.Item Optimizing multi-agent dynamics for underwater tactical applications.(2011-05-12T15:55:02Z) Yu, Albert R.; Marks, Robert J.; Engineering.; Baylor University. Dept. of Electrical and Computer Engineering.Large groups of autonomous agents, or swarms, can exhibit complex emergent behaviors that are difficult to predict and characterize from their low-level interactions. These emergent behaviors can have hidden implications for the performance of the swarm should the operational theater be perturbed. Thus, designing the optimal rules of operation for coordinating these multi-agent systems in order to accomplish a given task often requires simulations or expensive implementations. This thesis project examines swarm dynamics and the use of inversion to optimize the rules of operation of a large group of autonomous agents in order to accomplish missions of tactical relevance: specifically missions concerning underwater frequency-based standing patrols and point-defense between two competing swarms. Modified genetic algorithms and particle swarm optimization are utilized in the inversion process, producing various competing tactical responses and patrol behaviors. Swarm inversion is shown to yield effective and often creative solutions for guiding swarms of autonomous agents.