Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

Abstract

Abstract. Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

Optimization of Adaptive Genetic Algorithm Parameters in Traveling Salesman Problem

“…The main issue in the TSP case is how the seller can design his itinerary to visit multiple cities, where the distance from one city to another is known to reach a minimum total distance, and the seller can only visit that city once. There are various methods to solve the TSP problem, including the greedy algorithm (Wu & Fu, 2020), brute force algorithm (Violina, 2021), hillclimbing method (Fronita, Gernowo, & Gunawan, 2018), ant algorithm (Chen, Tan, Qian, & Chen, 2018), and genetic algorithm. The problem faced in TSP is how to find the minimum total distance.…”

Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot

“…It can move forward, backward, slide sideways and rotate in a fixed position so that it is more effective in maneuvering [47][48]. Therefore, this study hypothesizes that holonomic can simplify and accelerate mobile robots to maneuver in all directions and follow the application of Dijkstra's algorithm for the effectiveness and speed of distributing goods in warehouses [49] After the path has been mapped, the next step is finding the shortest path in each route on the path map [51]. Searching for the shortest route becomes very important in path planning because it is related to delay [52].…”