2,557 research outputs found
Cosolver2B: An Efficient Local Search Heuristic for the Travelling Thief Problem
Real-world problems are very difficult to optimize. However, many researchers
have been solving benchmark problems that have been extensively investigated
for the last decades even if they have very few direct applications. The
Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to
provide a more realistic model. TTP targets particularly routing problem under
packing/loading constraints which can be found in supply chain management and
transportation. In this paper, TTP is presented and formulated mathematically.
A combined local search algorithm is proposed and compared with Random Local
Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite
promising since new better solutions were found.Comment: 12th ACS/IEEE International Conference on Computer Systems and
Applications (AICCSA) 2015. November 17-20, 201
Using 2-Opt based evolution strategy for travelling salesman problem
Harmony search algorithm that matches the (µ+ 1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions
Water Flow-Like Algorithm with Simulated Annealing for Travelling Salesman Problems
Water Flow-like Algorithm (WFA) has been proved its ability obtaining a fast and quality solution for solving Travelling Salesman Problem (TSP). The WFA uses the insertion move with 2-neighbourhood search to get better flow splitting and moving decision. However, the algorithms can be improved by making a good balance between its solution search exploitation and exploration. Such improvement can be achieved by hybridizing good search algorithm with WFA. This paper presents a hybrid of WFA with various three neighbourhood search in Simulated Annealing (SA) for TSP problem. The performance of the proposed method is evaluated using 18 large TSP benchmark datasets. The experimental result shows that the hybrid method has improved the solution quality compare with the basic WFA and state of art algorithm for TSP
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
Recent works using deep learning to solve the Traveling Salesman Problem
(TSP) have focused on learning construction heuristics. Such approaches find
TSP solutions of good quality but require additional procedures such as beam
search and sampling to improve solutions and achieve state-of-the-art
performance. However, few studies have focused on improvement heuristics, where
a given solution is improved until reaching a near-optimal one. In this work,
we propose to learn a local search heuristic based on 2-opt operators via deep
reinforcement learning. We propose a policy gradient algorithm to learn a
stochastic policy that selects 2-opt operations given a current solution.
Moreover, we introduce a policy neural network that leverages a pointing
attention mechanism, which unlike previous works, can be easily extended to
more general k-opt moves. Our results show that the learned policies can
improve even over random initial solutions and approach near-optimal solutions
at a faster rate than previous state-of-the-art deep learning methods.Comment: To appear in Proceedings Machine Learning Research - ACML 202
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