Browsing by Author "Eberhart, Russ"

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    Human Fitness Functions
    (IEEE, 2015-09) Christopher, Lauren; Reynolds, Joshua; Crespo, Jonah; Eberhart, Russ; Shaffer, Patrick; Department of Electrical and Computer Engineering, School of Engineering and Technology
    "Be careful what you measure" is a management adage that applies to Particle Swarm Optimization (PSO) and is especially important with Humans in the Swarm. PSO has been applied to the autonomous asset management problem in electronic warfare where the speed provides fast optimization of frequency allocations for receivers and jammers in highly complex and dynamic environments in our previous work. In this optimization problem, one key part of the fitness is adapted by the human: the 2D (and future 3D) battlefield environment. This paper explores the use of the human in the fitness function, adapting to the battlefield conditions as the PSO is acting. Two aspects of dynamic human influence will be discussed: Simple geometric zones and pheromone influenced zones.
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    Using computational swarm intelligence for real-time asset allocation
    (IEEE, 2015-05) Reynolds, Joshua; Christopher, Lauren; Eberhart, Russ; Shaffer, Patrick; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    Particle Swarm Optimization (PSO) is especially useful for rapid optimization of problems involving multiple objectives and constraints in dynamic environments. It regularly and substantially outperforms other algorithms in benchmark tests. This paper describes research leading to the application of PSO to the autonomous asset management problem in electronic warfare. The PSO speed provides fast optimization of frequency allocations for receivers and jammers in highly complex and dynamic environments. The key contribution is the simultaneous optimization of the frequency allocations, signal priority, signal strength, and the spatial locations of the assets. The fitness function takes into account the assets' locations in 2 and 3 dimensions maximizing their spatial distribution while maintaining allocations based on signal priority and power. The fast speed of the optimization enables rapid responses to changing conditions in these complex signal environments, which can have real-time battlefield impact. Initial results optimizing receiver frequencies and locations in 2 dimensions have been successful. Current run-times are between 300 (3 receivers, 30 transmitters) and 1000 (7 receivers, 30 transmitters) milliseconds on a single-threaded x86 based PC. Statistical and qualitative tests indicate the swarm has viable solutions, and finds the global optimum 99% of the time on a test case. The results of the research on the PSO parameters and fitness function for this problem is demonstrated.