Genetic Routing of Non-Intersecting Primary and Backup Paths
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Solution Overview
Problem
Conventional routing methods, such as Dijkstra's algorithm, are inadequate for identifying multiple non-intersecting routes in networks due to their greedy nature, which can lead to high resource costs and the exclusion of viable secondary routes, posing challenges in network resilience and reliability.
Innovation Solution
A method using a genetic algorithm to iteratively select, crossover, and mutate primary routes in a population set, ensuring non-intersecting primary and secondary routes are determined by excluding edges in the primary route, with a computer system implementing this process to optimize edge weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a greedy route-finding algorithm is used to determine the shortest primary route, then the primary route achieves minimum resource cost, but the algorithm fails to identify viable non-intersecting secondary routes
Solution Approach 1:
The patent segments the route-finding problem into two distinct phases: first finding the optimal primary route using a greedy algorithm, then separately finding a non-intersecting secondary route by excluding edges used in the primary route. This segmentation allows each route to be optimized independently for its specific requirements.
Solution Approach 2:
The patent performs preliminary action by first determining the primary route and recording its used edges before searching for the secondary route. This preliminary identification of constrained edges enables the secondary route search to automatically avoid intersections, ensuring reliability without re-optimizing the primary route.
2Reliability
If multiple independent non-intersecting routes are required for network resilience, then reliability is improved, but the resource cost and complexity of route determination increases
Solution Approach 1:
The patent applies dynamics by making the graph structure adaptive during the route-finding process. When finding the secondary route, the system dynamically modifies the graph by excluding edges used in the primary route, creating a constrained subgraph that guides the search for non-intersecting paths without requiring complex multi-constraint algorithms.
Solution Approach 2:
The patent uses an intermediary approach by introducing a modified graph representation that acts as a mediator between the primary and secondary route requirements. This intermediary structure encodes the non-intersection constraint, allowing standard shortest-path algorithms to automatically produce valid secondary routes without direct complex interaction between route constraints.
3Speed
If conventional greedy algorithms are used for route finding, then computational speed is maintained, but the ability to identify optimal non-intersecting route pairs is lost
Solution Approach 1:
The patent segments the optimization problem into two independent greedy optimizations: first optimizing the primary route independently, then optimizing the secondary route independently on a constrained graph. This segmentation preserves the computational efficiency of greedy algorithms while achieving optimal route pairs for the non-intersection constraint.
Solution Approach 2:
The patent performs preliminary action by first establishing the primary route and its edge constraints before initiating the secondary route search. This preliminary step enables subsequent greedy optimization to focus exclusively on finding the best non-intersecting secondary route, maintaining speed while ensuring optimality within the constraint framework.
Data Source
AI summary
A computer implemented method of determining non-intersecting primary and secondary routes between source and destination nodes in a communications network. The communication network represented by a graph data structure of nodes and edges, the edges having a weight corresponding to a resource involved in traversing the edged. The method including: defining a population set of primary routes, wherein each primary route in the population set identifies a secondary route based on a greedy route-finding algorithm applied to the graph having edges in the primary route excluded from the graph, such that the primary and secondary routes are non-intersecting routes through the graph; applying a genetic algorithm to the population set to iteratively select and crossover one or more pairs of primary routes in the population set; and selecting, from the population set, a primary route and corresponding secondary route having a lowest aggregate weight of edges.