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Computational Intelligence Algorithms for Optimized Vehicle Routing Applications in Geographic Information Systems
| Content Provider | CiteSeerX |
|---|---|
| Author | Rice, Michael |
| Abstract | This project seeks to explore the application of two developing algorithmic paradigms from the field of computational intelligence towards optimized vehicle routing within geographic information systems (GIS). Ant Colony Optimization (ACO) is a type of multi-agent, swarm-based algorithm designed to mimic the emergent problem-solving behavior of real ants within a colony. Genetic Algorithms (GA) are another natureinspired type of algorithm designed for evolving optimal or near-optimal solutions to a problem through the use of techniques based on natural selection, crossover, and mutation. The goal of this project is to implement and test a hybrid version of these two algorithms, aimed at evolving agents (ants) for optimized routing within the vehicle routing program. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Geographic Information System Optimized Vehicle Routing Application Computational Intelligence Algorithm Natureinspired Type Swarm-based Algorithm Ant Colony Optimization Computational Intelligence Towards Emergent Problem-solving Behavior Natural Selection Algorithmic Paradigm Near-optimal Solution Hybrid Version Real Ant |
| Content Type | Text |