Application of ant colony optimization algorithm to determine optimal value in choosing tourist attractions in Bangkalan -Madura (original) (raw)
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International Journal of Engineering Research and Applications (IJERA), 2017
Everyone needs a tourist attraction to add knowledge and experience. Jember city is one of the city that has many tourist attractions. The tourists who are going to the attractions need a lot of paths to take, so it takes a method to choose the shortest path. There are some solutions to solve the shortest path search which has been done a lot. However, the solution has not provided satisfactory success, so various improvement efforts need to be done. In this research, the shortest path search is using Ant Colony System (ACS) method. The ACS method is one of the heuristic methods that can provide the optimal solution to find the shortest path on the way from the nest to the food source or destination. The performance process of the ACS is to move from one node to the next with high pheromone evaporation to choose the best and optimal solution based on the compilation of the visit path from each ant to each city. Ant colony travels continuously until all the cities one by one which is visited or have occupied the tabu list. This method uses tabu list to store a set of recently evaluated solutions, and the results will be adjusted first with the contents of the taboo list to see if the solution has been achieved or not. When the solution is reached then the solution will not be evaluated again in the next iteration. Various trials are underway to prevent the retrieval process in the available traced solution space. The results show that ACS method developed in this research is able to find the determination in determining the journey with the shortest path from and to the tourist object in Jember so that it can be considered in decision making to show the path that will be passed and the efficiency of time, cost and energy. The quality of success is obtained using parameters q0 = 0.1, α = 0.2, β = 2, ρ = 0.2 with the number of ants and many cycles.
Journal of Physics: Conference Series 1477 (2020) 022019, 2020, 2020
The fastest route search is a problem to find routes having a relatively small or empty number of congestion or density so that the required travel time is faster to get to a location. One of them is the route to the tourist attraction in Jember regency, East Java has many relatively solid lines. Some researchers have done a lot of research on finding the shortest path using Ant Colony System (ACS) method. However, the ACS method has a weakness, where more ants pass through a path, the clearer the footprint but the volume of ants passing through the path is also longer. Therefore in this research use ant adaptive on ACS method to find the fastest path to the tourist location in Jember Regency. Due to the increasing number of densities, it takes longer to get to the food source. In this study, ant adaptive for ACS method is used to determine the optimal number of ants in searching for the fastest path to the tourist attraction in Jember District, East Java. Various trials are done to prevent the search process of resolving the already traced solution space to find the fastest path. The fastest path does not have to have the shortest path but has the fastest time with the paths having relatively little or no density at all. The results showed that ant adaptive for ACS method developed in this research is able to find the optimal number of ants in determining the fastest paths to the tourist object so that the search result of the fastest route of tourist attraction in Jember Regency in accordance with the reality of the passable path and has a density relatively small or almost nonexistent. The success rate was conducted on 8 tourist objects in Jember with 98 paths tested using road data in Jember regency, East Java with the fastest path solution influenced by the closer to the optimum distance, the more number of ants influenced.
Journal of Physics: Conference Series, 2019
Ant Colony is a methodology produced by observing ants. In this algorithm, ants as agents assigned to find solutions to an optimization problem, like to find the optimal solution on Traveling Salesman Problem (TSP). Therefore, the research will apply ant colony algorithm to search the shortest path of tourism in Palembang City, covering tourist destinations, culinary, hotels and handicrafts. The results of this study, will provide ease of tourists in accessing tourist destination information and information about travel routes easily via mobile applications with android operating system and use of Google Maps API.
Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2019
Jember Regency has a place that contains elements of cultural, historical, educational, recreational values that are spread out, especially in the field of natural tourism. However, these tourist tourism object received less attention from the local tourism agency so that little information was received by the public regarding some of the attractions in Jember Regency. In addition, to go to the tourist attraction must pass through areas with steep roads so that a map is needed to get there that can provide information on road conditions that can be passed and does not require a long time. Therefore, this study has designed a fastest track search application that can help tourists to get to know various kinds of tourist objects in Jember Regency along with routes that must be passed to get to or from tourist attractions in Jember Regency. With a short time. In the application of the fastest track search, an optimization method is needed which can provide the optimum distance effect with a short time. In this study using the effect of the number of ants on the Ant Colony System (ACS) method to produce the optimal number of ants in finding the fastest path to the tourist attraction in Jember Regency. The purpose of this study is to provide the fastest route solution to the tourist sites in a short time and help introduce various kinds of tourist objects in Jember Regency that are not widely known by tourists. So that with this system it can simplify and accelerate the tourists in finding and obtaining information about the location of tourist attractions with the fastest route found in Jember Regency
Use of Ant Colony Optimization Algorithm for Determining Traveling Salesman Problem Routes
Jurnal Matematika "MANTIK"
Ant Colony Optimization is one of the meta-heuristic methods used to solve combinatorial optimization problems that are quite difficult. Ant Colony Optimization algorithm is inspired by ant behavior in the real world to build the shortest path between food sources and their nests. Traveling Salesman Problem is a problem in optimization. Traveling Salesman Problem is a problem to find the minimum distance from the initial node to the whole node with each node must be visited exactly once and must return to the initial node. Traveling Salesman Problem is a non-deterministic polynomial-time complete problem. This research discusses the solution of the Traveling Salesman Problem using the Ant Colony Optimization algorithm and also using the exact algorithm. The results showed that the greater the size of the Traveling Salesman Problem case, the longer the execution time required. The results also showed that the execution times of the Ant Colony Optimization are much faster than the exe...
Modified Ant Colony Optimization for Solving Traveling Salesman Problem
International Journal of Engineering & Computer Science IJECS-IJENS Vol:13 No:05, 2013
This paper presents a new algorithm for solving the Traveling Salesman Problem (NP-hard problem) using pheromone of ant colony depends on the pheromone and path between cites. TSP is a problem in theoretical computer science which is very hard to solve a number of real-world problems can be formalized as TSP problems, and ants of the colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP and improves ACO by taking under consideration the overall path of cities in candidate list. Sometimes choosing minimum distance isn't a guaranty that's a best path. The experimental results on large set of samples indicate that the new approach is better than the original one for finding optimal solutions of the (TSP) in less time as compared to results achieved by applying basic idea of ACO algorithm.
Traveling Salesman Problem (TSP) is a well-known, popular and extensively studied problem in the field of combinatorial optimization and attracts computer scientists, mathematicians and others. Its statement is deceptively simple, but yet it remains one of the most challenging problems in operational research. It also an optimization problem of finding a shortest closed tour that visits all the given cities within the shortest time. Several optimization techniques have been used to solve the Travelling Salesman Problems such as; Ant Colony Optimization Algorithm (ACO), Genetic Algorithm (GA) and Simulated Annealing, but comparative analysis of ACO and GA in TSP has not been carried out. In this paper, an evaluation of performance was made between the Ant Colony Optimization (ACO) and Genetic Algorithm (GA) in optimizing the nearest city and distance covered by the traveler. The simulation was done and carried out on Matlab 7.10a. The results generated show that GA is a well-accepted simulator in solving the Travelling Salesman Problem, as it out performs the ACO in terms of simulation time and distance covered. Hence GA is a useful tool in solving the travelling salesman problem, as it optimizes better than the ACO.
Analysis for Travelling Salesman Problem using Improved Ant Colony Optimization Algorithm
2019
Ant Colony Optimization (ACO) is a recent algorithm used for solving optimization problems and is the model on the behavior of real ant colonies. It has been used exclusively for solving problems in the combinatorial optimization domain. Traveling salesman problem (TSP) is one of the well-known and extensively studied problems in combinational optimization and used to find the shortest roundtrip of minimal total cost visiting each given city (node) exactly once and it can be applied to solve many practical problems in real life. ACO is a good search capability and a high-performance computing method for TSP. But, the traditional ACO has many drawbacks such as stagnation behavior, trapping in local optimal and premature convergence. This paper implements and evaluates a specialized version of ant colony optimization capable of searching in travelling salesman problems and evaluates its performance under a range of conditions and test cases. The proposed system is an improved ant colo...