Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

Abstract

The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

Meta-Heuristic Algorithms for Learning Path Recommender at MOOC

“…However, we need to test other research directions to improve the quality of the proposed algorithms. Some potential research directions include but are not limited to Chaotic Random Spare ACO [32], another EA approach like Evolutionary biogeographybased [33], hybrid method for EA [34]. Our future work is also to build a model that allows the personalization of learners' learning paths based on demand and similarity among learners, user feedback, etc.…”