@inproceedings{b6d3b4bab41246918d129eb5a69f7103,
title = "Nature-inspired computational model for solving bi-objective traveling salesman problems",
abstract = "Bi-objective Traveling Salesman Problem (BTSP) is an NP-hard problem in the combinatorial optimization, which is also important question in the field of operations research and theoretical computer science. Genetic Algorithm (GA) is one type of efficient methods for solving NP-hard problems. However, GA-based algorithms suffer high time computational complexity, low stability and the premature convergence for solving BTSP. This paper proposes an improved method of genetic algorithm based on a novel nature-inspired computational model to solve these problems. The initialization of population of proposed algorithm is first optimized by the prior knowledge of Physarum-inspired computational model (PCM) in order to enhance the computational speed and stability. Then the hill climbing method (HC) is used to increase the diversity of the individuals and avoid falling into the local optimum. A series of experiments are conducted and results show that our proposed algorithm can achieve the better performance.",
keywords = "Bi-objective traveling salesman problem, Hill climbing, NSGA\_II, Physarum",
author = "Xuejiao Chen and Zhengpeng Chen and Yingchu Xin and Xianghua Li and Chao Gao",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97310-4\_25",
language = "英语",
isbn = "9783319973098",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "219--227",
editor = "Xin Geng and Byeong-Ho Kang",
booktitle = "PRICAI 2018",
}