Application of Neuro-Fuzzy system to solve Traveling Salesman Problem

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) in solving the traveling salesman problem. Takagi-Sugeno-Kang neuro-fuzzy architecture model is used for this purpose. TSP, although, simple to describ

A novel metaheuristic for traveling salesman problem: blind mole-rat algorithm

Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

A novel metaheuristic for traveling salesman problem: blind mole-rat algorithm

Gezgin Satıcı Problemi (GSP), başlangıç ve bitiş şehirleri aynı olan ve her şehrin sadece bir kez ziyaret edildiği minimum mesafeli turu bulma problemidir. Şehir sayısı arttıkça, kesin yöntemler ile kabul edilebilir sürelerde bir optimum çözüm bulunması zordur. Bu nedenle, son elli yılda GSP’nin çözümü için doğadan ve biyolojiden esinlenen birçok meta-sezgisel yöntem geliştirilmiştir. Bu çalışmada, toprak altındaki bireysel tünel sistemlerinde yaşayan kör farelerin toprak altındaki engelleri geçme stratejisinden esinlenilerek GSP’nin çözümü için yeni bir meta-sezgisel tasarlanmıştır. Geliştirilen yönteme Kör Fare Algoritması adı verilmiştir. Bu yeni sezgisel ile farklı boyutlardaki simetrik test veri setleri için deneyler yapılmış ve sonuçları bilinen en iyi sonuçlar ile kıyaslanmıştır. Önerilen meta-sezgisel henüz literatürdeki diğer algoritmalarla yarışabilecek düzeyde olmamasına rağmen, başlangıç test çözümlerinin umut verici olduğu söylenebilir.Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

A Perturbed Self-organizing Multiobjective Evolutionary Algorithm to solve Multiobjective TSP

Travelling Salesman Problem (TSP) is a very important NP-Hard problem getting focused more on these days. Having improvement on TSP, right now consider the multi-objective TSP (MOTSP), broadened occurrence of travelling salesman problem. Since TSP is NP-hard issue MOTSP is additionally a NP-hard issue. There are a lot of algorithms and methods to solve the MOTSP among which Multiobjective evolutionary algorithm based on decomposition is appropriate to solve it nowadays. This work presents a new algorithm which combines the Data Perturbation, Self-Organizing Map (SOM) and MOEA/D to solve the problem of MOTSP, named Perturbed Self-Organizing multiobjective Evolutionary Algorithm (P-SMEA). In P-SMEA Self-Organizing Map (SOM) is used extract neighborhood relationship information and with MOEA/D subproblems are generated and solved simultaneously to obtain the optimal solution. Data Perturbation is applied to avoid the local optima. So by using the P-SMEA, MOTSP can be handled efficiently. The experimental results show that P-SMEA outperforms MOEA/D and SMEA on a set of test instances

Parallelized neural network system for solving Euclidean traveling salesman problem

We investigate a parallelized divide-and-conquer approach based on a self-organizing map (SOM) in order to solve the Euclidean Traveling Salesman Problem (TSP). Our approach consists of dividing cities into municipalities, evolving the most appropriate solution from each municipality so as to find the best overall solution and, finally, joining neighborhood municipalities by using a blend operator to identify the final solution. We evaluate the performance of parallelized approach over standard TSP test problems (TSPLIB) to show that our approach gives a better answer in terms of quality and time rather than the sequential evolutionary SOM

Simulation design of trajectory planning robot manipulator

Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.