Traveling salesman problem based on simulated annealing
Başlık çevirisi mevcut değil.
- Tez No: 796357
- Danışmanlar: PROF. DR. OSMAN NURİ UÇAN
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2022
- Dil: İngilizce
- Üniversite: Altınbaş Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Bilişim Teknolojileri Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 62
Özet
The traveling salesman's problem, specifically the scenario in which several points need to be determined to reach them at the right time and time, was the subject of this study. The traveling salesman's problem was addressed, along with its solution using the ant algorithm. The ant algorithm is used in this approach to solve the traveling salesman problem. The ACO method is used to find a solution to the traveling salesman problem. This helps significantly reduce the amount of time that the algorithm needs to spend performing calculations. A comparative experiment was conducted using MMACO as the primary method. If all you must do are complete tasks that involve parallel processing of large amounts of data, this will save you a significant amount of time. We use the representation that occupies the largest possible space so that we can manage the space between the points to be reached. The proposed approach is a hybrid approach that combines a unique placement technique and a multi-population that is used to develop the order in which objects are to be delivered and to know the corresponding layer type used in the placement procedure. A heuristic is used to find out the best point of each access to go to in the shortest possible time. Various methods of solving normative problems are put through a series of difficult tests to see which one is better. There are 500 different examples in the criteria set. They range from having little to no heterogeneity. Each of these different approaches is evaluated along with many other problemsolving methodologies. When compared to other approaches described in the literature, computational experiments demonstrate that the method not only achieves the best overall results but also performs well in all sorts of instance classes. This is evidenced by the fact that the method outperforms them all.
Özet (Çeviri)
The term Ant Colony Optimization, or ACO for short, refers to a family of heuristic search methods that have been effectively utilized in the process of resolving combinational optimization (CO) issues. The traveling salesman problem, sometimes known as the TSP, is widely considered to be one of the most significant combinatorial problems. ACO possesses an excellent search capacity for resolving optimization issues. But despite this, it still has a few flaws, the most notable of which are its behavior of stagnation its lengthy processing time, and the premature completion problem of the fundamental ACO algorithm on TSP. When the problems being evaluated get more complicated, the problems will become more obvious. Ant colony optimization, often known as ACO, is a high-performance computer approach that has been used to solve a variety of combinatorial optimization problems, including the (TSP). The (TSP) is one of the most well-known combinatorial optimizations problems, and it has a diverse range of application contexts. The ACO algorithm has very good search capacity for optimization problems, but it is still a computational bottleneck because it takes too much time to converge and gets stuck in local optima when trying to find an optimal solution for TSP problems. Although the ACO algorithm has a very great search capacity for optimization problems, this issue still exists. It was decided to use the candidate set technique to achieve high convergence speeds. We propose a dynamic updating method for heuristic parameters that is based on entropy to increase performance in the solution of TSP problems. The traveling salesman problem, commonly abbreviated as TSP, is a well-known and significant combinatorial optimization issue. Other names for this problem are traveling salesman's problem and TSP. The objective is to locate the quickest route that makes one stop in each city on the provided list, then circles back to the location where the journey began. Despite the seeming ease of the problem description, finding a solution to the TSP is challenging since it falls under the category of NP-complete problems.
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