A Solving Route Optimization of Airplane Travel Problem Use Artificial Bee Colony Algorithm

Abstract

The Traveling Salesman Problem (TSP) is very popular in combinatoric optimization. The TSP problem is finding the optimal route from several cities where the distance between cities is known, and a salesman must visit each city exactly once and return to the origin city. The goal is to find a route with a minimum total distance. This problem is known as a non-deterministic polynomial hard (NP-hard) problem, which means the computation time to find a solution increases exponentially with the size of the problem. NP-Hard problems can be solved by using heuristic methods where the solution obtained is good enough (does not guarantee the most optimal solution) in a reasonable time. One of the most recent variants of TSP problem is finding the cheapest flight routes to several cities, which is part of the Traveling Salesman Challenge 2.0 (TSC 2.0) 2018 competition . This paper reports our study of implementing an artificial bee colony (ABC) algorithm for the TSC 2.0 problem. ABC algorithm is chosen based on its superiority over other algorithms in several optimization problems. The algorithm is implemented in a hyper-heuristic form. Several combinations of swap operators are used to find the best combination result. The experimental result shows that the ABC algorithm can solve the TSC 2.0 problem with a fairly good performance by producing a savings cost of 54.6% from the initial solution and 26% compared to the Genetic Algorithm.