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

Different initial solution generators in genetic algorithms for solving the probabilistic traveling salesman problem

[[abstract]]The probabilistic traveling salesman problem (PTSP) is a topic of theoretical and practical importance in the study of stochastic network problems. It provides researchers with a modeling framework for exploring the stochastic effects in routing problems. This paper proposed three initial solution generators (NN1, NN2, RAN) under a genetic algorithm (GA) framework for solving the PTSP. A set of numerical experiments based on heterogeneous and homogeneous PTSP instances were conducted to test the effectiveness and efficiency of the proposed algorithms. The results from the heterogeneous PTSP show that the average E[tau] values obtained by the three generators under a GA framework are similar to those obtained by the "Previous Best," but with an average computation time saving of 50.2%. As for the homogeneous PTSP instances, NN1 is a relatively better generator among the three examined, while RAN consistently performs worse than the other two generators in terms of average E[tau] values. Additionally, as compared to previously reported studies, no one single algorithm consistently outperformed the others across all homogeneous PTSP instances in terms of the best E[tau] values. The fact that no one initial solution generator consistently performs best in terms of the E[tau] value obtained across all instances in heterogeneous cases, and that the performance of each examined algorithm is dependent on the number of nodes (n) and probability (p) for homogeneous cases, suggest the possibility of context- dependent phenomenon. Finally, to obtain valid results, researchers are advised to include at least a certain amount of test instances with the same combination of n and p when conducting PTSP experiments. (C) 2010 Elsevier Inc. All rights reserved.[[note]]SC