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

Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

[Extract] Population based optimization algorithms are the techniques which are in the set of the nature based optimization algorithms. The creatures and natural systems which are working and developing in nature are one of the interesting and valuable sources of inspiration for designing and inventing new systems and algorithms in different fields of science and technology. Evolutionary Computation (Eiben& Smith, 2003), Neural Networks (Haykin, 99), Time Adaptive Self-Organizing Maps (Shah-Hosseini, 2006), Ant Systems (Dorigo & Stutzle, 2004), Particle Swarm Optimization (Eberhart & Kennedy, 1995), Simulated Annealing (Kirkpatrik, 1984), Bee Colony Optimization (Teodorovic et al., 2006) and DNA Computing (Adleman, 1994) are among the problem solving techniques inspired from observing nature. In this chapter population based optimization algorithms have been introduced. Some of these algorithms were mentioned above. Other algorithms are Intelligent Water Drops (IWD) algorithm (Shah-Hosseini, 2007), Artificial Immune Systems (AIS) (Dasgupta, 1999) and Electromagnetism-like Mechanisms (EM) (Birbil & Fang, 2003). In this chapter, every section briefly introduces one of these population based optimization algorithms and applies them for solving the TSP. Also, we try to note the important points of each algorithm and every point we contribute to these algorithms has been stated. Section nine shows experimental results based on the algorithms introduced in previous sections which are implemented to solve different problems of the TSP using well-known datasets

Distributed Simulated Annealing with MapReduce

Simulated annealing’s high computational intensity has stimulated researchers to experiment with various parallel and distributed simulated annealing algorithms for shared memory, message-passing, and hybrid-parallel platforms. MapReduce is an emerging distributed computing framework for large-scale data processing on clusters of commodity servers; to our knowledge, MapReduce has not been used for simulated annealing yet. In this paper, we investigate the applicability of MapReduce to distributed simulated annealing in general, and to the TSP in particular. We (i) design six algorithmic patterns of distributed simulated annealing with MapReduce, (ii) instantiate the patterns into MR implementations to solve a sample TSP problem, and (iii) evaluate the solution quality and the speedup of the implementations on a cloud computing platform, Amazon’s Elastic MapReduce. Some of our patterns integrate simulated annealing with genetic algorithms. The paper can be beneficial for those interested in the potential of MapReduce in computationally intensive nature-inspired methods in general and simulated annealing in particular.https://digitalcommons.chapman.edu/scs_books/1016/thumbnail.jp

Implementasi Cuckoo Search Algorithm pada Travelling Salesman Problem Menggunakan Levy Flight

ABSTRAKSI: Cuckoo Search (CS) merupakan salah satu dari Nature-Inspired Algorithms memiliki performa yang mengesankan pada permasalahan optimasi untuk kasus bilangan real. CS pun menjanjikan performa yang baik untuk kasus permasalahan diskret, seperti masalah kombinatorial Travelling Salesman Problem karena adanya operasi Lévy Flights yang menjadi operator ampuh dalam pencarian.Tugas Akhir ini memperkenalkan CS untuk menyelesaikan permasalahan TSP yang bersifat simetris. Pengembangan utama CS untuk TSP ini berinti pada operasi Lévy Flights dengan dikembangkan operasi inverse mutation. Hasil pengujian menunjukkan CS dapat bekerja dengan baik pada TSP. Dari empat pengujian, CS memberikan solusi optimum untuk kasus pertama dan keempat, pada kasus kedua dan ketiga CS memberikan solusi yang cukup baik.Kata Kunci : cuckoo search, travelling salesman problem, lévy flights, inverse mutationABSTRACT: Cuckoo Search(CS) is one of many Nature-Inspired Algorithms that has an impressive performance on optimization problems for the case of real numbers. CS also promises good performance for the case of discrete problems such as combinatorial problem the Travelling Salesman Problem due to Levy Flights operation that become a powerful operator in searching process.This Final Assignment introduces CS to complete the one-dimensional symmetric TSP. CS major development for this TSP lies in Levy Flights, which is modified with inverse mutation.The test results indicate that CS also performs well for the TSP. From the four cases examined, CS managed to find a solution for the first dan the last case, CS gave a good result for the second dan the third case.Keyword: cuckoo search, travelling salesman problem, lévy flights, inverse mutatio

Optimal multi-objective discrete decision making using a multidirectional modified Physarum solver

This paper will address a bio-inspired algorithm able to incrementally grow decision graphs in multiple directions for discrete multi-objective optimization. The algorithm takes inspiration from the slime mould Physarum Polycephalum, an amoeboid organism that in its plasmodium state extends and optimizes a net of veins looking for food. The algorithm is here used to solve multi-objective Traveling Salesman and Vehicle Routing Problems selected as representative examples of multi-objective discrete decision making problems. Simulations on selected test case showed that building decision sequences in two directions and adding a matching ability (multidirectional approach) is an advantageous choice if compared with the choice of building decision sequences in only one direction (unidirectional approach). The ability to evaluate decisions from multiple directions enhances the performance of the solver in the construction and selection of optimal decision sequences