EM Resource Allocation via Particle Swarm Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional resource allocation methods in electromagnetic (EM) systems are limited in maximizing signal traffic and prioritizing signals in real-time, often underutilizing preserved signal space and failing to serve priority signals effectively.
Innovation Solution
A system and method using evolutionary algorithms, specifically particle swarm optimization, to dynamically assign EM resources by scanning the EM spectrum, optimizing fitness functions based on signal parameters and resource constraints, and iteratively improving solutions to select optimal resource allocations in near real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional resource allocation methods preserve a portion of signal space for priority calls, then priority signals are protected, but the preserved space is underutilized and overall signal traffic is reduced
Solution Approach 1:
The patent implements dynamic resource allocation where the signal space reserved for priority calls is not fixed but adapts in real-time based on current traffic conditions. The system continuously monitors signal characteristics and dynamically adjusts which frequencies are reserved for priority calls versus available for general traffic, allowing the preserved space to be utilized more efficiently when priority traffic is low while maintaining protection when needed.
2Ease of operation
If channels are assigned on a first-come-first-served basis and maintained until traffic ends, then resource assignment is simple, but priority signals are under-served and response time is delayed
Solution Approach 1:
The system incorporates continuous feedback mechanisms that monitor signal priorities, traffic patterns, and channel utilization in real-time. This feedback drives the evolutionary algorithm to adjust channel assignments dynamically, ensuring priority signals receive appropriate service while maintaining operational efficiency. The feedback loop allows the system to learn from past allocations and improve future assignments without complex manual intervention.
3Ease of manufacture
If traditional allocation methods are used, then implementation is straightforward, but the system cannot maximize desired response function on a near real-time basis
Solution Approach 1:
The patent replaces traditional mechanical or rule-based resource allocation mechanisms with an evolutionary algorithm that uses computational intelligence to optimize allocations. This substitution enables near real-time optimization of the desired response function by simulating evolutionary processes that adapt to changing conditions, achieving both high productivity and reasonable implementation complexity through software-based optimization rather than complex hardware or manual systems.
Data Source
AI summary
Embodiments of a system and a method for assigning a plurality of resources to a plurality of targets in near real-time are described herein. An evolutionary algorithm such as a particle swarm algorithm iteratively evaluates a predetermined fitness function characteristic of a system including the plurality of resources to find solutions containing optimal resource assignments. Resource parameters are changed based on the assignments to operate on the targets.