Recommending Optimal Tour for Groups using Ant Colony Optimization

Tour recommendation is one of the special cases of travelling salesman problem. Many researchers provided near optimal solution to this problem. However, literature shows that tours are mainly recommended to specific persons not to the group of people. This paper provides the different algorithmic approaches to give near optimal tours for the group of people. For such type of problem, it is essential to find the point of interest (POI) of each person in a group. The problem is quite intricate because all people of the group should be satisfied from the tour obtained. Four algorithmic implications are adopted in this work to find optimal tours - exhaustive search, greedy algorithm, dynamic programming and ant colony optimization (ACO). Optimal tour for group not only means shortest length path (total cost) but also satisfaction value of each person from tour obtained. Satisfaction value is a like or dislike of each person in a group from POIs considered in a tour. It was observed that ACO provide better results i.e. better combination of total cost value and satisfaction value as compared to other algorithms.