Browsing by Subject "asset allocation"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Particle Swarm Optimization in the dynamic electronic warfare battlefield
    (2017-04-27) Witcher, Paul Ryan; Christopher, Lauren
    This research improves the realism of an electronic warfare (EW) environment involving dynamic motion of assets and transmitters. Particle Swarm Optimization (PSO) continues to be used to place assets in such a manner where they can communicate with the largest number of highest priority transmitters. This new research accomplishes improvement in three areas. First, the previously stationary assets and transmitters are given a velocity component, allowing them to change positions over time. Because the assets now have a starting position and velocity, they require time to reach the PSO solution. In order to optimally assign each asset to move in the direction of a PSO solution location, a graph-based method is implemented. This encompasses the second area of research. The graph algorithm runs in O(n^3) time and consumes less than 0.2% of the total measured computation time to find a solution. Transmitter location updates prompt a recalculation of the PSO, causing the assets to change their assignments and trajectories every second. The computation required to ensure accuracy with this behavior is less than 0.5% of the total computation time. The final area of research is the completion of algorithmic performance analysis. A scenario with 3 assets and 30 transmitters only requires an average of 147ms to update all relevant information in a single time interval of one second. Analysis conducted on the data collected in this process indicates that more than 95% of the time providing automatic updates is spent with PSO calculations. Recommendations on minimizing the impact of the PSO are also provided in this research.
  • Thumbnail Image
    Item
    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.