Using computational swarm intelligence for real-time asset allocation

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

Asset Allocation with Swarm/Human Blended Intelligence

PSO has been used to demonstrate the near-real-time optimization of frequency allocations and spatial positions for receiver assets in highly complex Electronic Warfare (EW) environments. The PSO algorithm computes optimal or near-optimal solutions so rapidly that multiple assets can be exploited in real-time and re-optimized on the fly as the situation changes. The allocation of assets in 3D space requires a blend of human intelligence and computational optimization. This paper advances the research on the tough problem of how humans interface to the swarm for directing the solution. The human intelligence places new pheromone-inspired spheres of influence to direct the final solution. The swarm can then react to the new input from the human intelligence. Our results indicate that this method can maintain the speed goal of less than 1 second, even with multiple spheres of pheromone influence in the solution space