Jaymer M. Jayoma
ABSTRACT
Inverse Ant Algorithm is an enhanced Ant Algorithm that covers real-world scenarios to avoid stagnation in finding the best path from the source to destination. This is done by incorporating rules and constraints that contributes to pheromone level concentration in the path which is the basis for its decision making on agent's choice of next move. Other modifications like path elimination rule was added to ensure that only short paths were eligible of the selection process of ants' next move as it traverses from the source to its destination. Through the path elimination rule modification the inverse ant algorithm was able to eliminate long distant paths as ants' choice for the next possible move and eventually return choices of shorter paths for the ants' selection process which the inverse ant algorithm with path elimination rule has successfully implemented which resulted to shorter best path and it avoid stagnation as rules and constraints are applied. However, even with its increase in efficiency, the current implementations uses the same pheromone concentration on the path as its initial value and agents deposit the same pheromone cost as agents traverse the path. In addition, the current implement uses the same pheromone evaporation cost overtime in each path which is not the actual defection in the real-world. In order to address this issue a modification is introduced to the current Inverse Ant Algorithm model which uses variable path rules and constraints that mimics real world scenarios in a road traffic network such as car length rules, traffic light delay rule, path speed limit rule, and path pheromone capacity rule. The enhancements made is applied to the current implementation in order to achieve reliability in implementing route optimization and to enhance its performance.
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Index Terms
- Enhanced Inverse Ant Algorithm with Mutable Path Pheromone Concentration
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