Experimental Investigation and Performance Analysis Comparison of Max-min ANT System and ANT System using TSPLIB Data Sets

Abstract

Background/Objectives: To analyse the performance of ant-colony optimization by comparing max-min ant system and ant system using TSPLIB datasets to reduce the time and provide the optimal result. Methods/Statistical Analysis: Ant Colony Optimization was being applied in TSP for optimizing the path to visit all the nodes in TSP. Working of ACO algorithm is to find out an optimal solution in a search space where finding the optimal solution is almost not a possible case. Findings: In this research, it has been given the number of cities that are in need to be travelled by salesman, and the distance between the cities will also be given. The sales persons are supposed to visit all the cities exactly once and they have to return to the source point or origin. Now the ants are allowed to travel between the nodes by applying the constraints and for choosing the next visit node using some heuristics and the pheromone quantity level as usual which is the ants indirect communication and this has been done iteratively and finally the maximum contained arc will be chosen as the best path to solve the Vehicle Routing Problem. Applications/Improvements: They have proposed a new algorithm called Ant Colony System-Software Project Scheduling Problem (ACS-SPSP) algorithm. The software project should be scheduled in terms of duration so that it should be finished in time and it should utilize the employee's skills and the cost associated with it. And also the employees can have more than one skill and their skills also should be utilized properly by considering the working time of the employees and the employees have been appointed to do a project which consists of several tasks.