A Structured Timing Itinerary Using an Augmented Swarm Intelligent Algorithm

There are well-known approaches which have been exercised to generate automated timing itinerary which is limited to secluded environment. The number of entity having individual characteristics increases, thus increases the complexity of the solution. As scheduling timing itinerary is a well-known NP-completeness problem, so there is no existing particular algorithm which can solve the time scheduling problem in polynomial time bounded by the number of entities present in the itinerary where multiple constraints between entities are to be satisfied. We present an augmented swarm intelligent algorithm called ASIA which has been applied into a structured time itinerary approach consists of an intelligent neighborhood selection strategy by exchanging the pair of scheduled lectures and with a faster running time improvement due to increased memory feeding and addition of code complexity. Also, an objective value or weight is defined to get better optimization output and the associated plot of objective value is drawn to compare the result of the proposed approach with meta-heuristic approaches. The result shows that using ASIA can deliver an optimal solution and proved to be an efficient than other well-known meta-heuristic approaches.