文献类型：期刊文献

英文题名：Mosquito Host-Seeking Algorithm Based on Random Walk and Game of Life

作者：Zhu, Yunxin[1]; Feng, Xiang[1,2]; Yu, Huiqun[1]

机构：[1] Department of Computer Science and Technology, East China University of Science and Technology, Shanghai, 200237, China; [2] Smart City Collaborative Innovation Center, Shanghai Jiao Tong University, Shanghai, 200240, China

年份：2018

卷号：10955 LNCS

起止页码：693

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20183505746875)

语种：英文

外文关键词：Simulated annealing - Artificial intelligence - Computation theory - Random processes - Ant colony optimization

摘要：Mosquito Host-seeking Algorithm (MHSA) is a novel bionic algorithm. It simulates the behavior of mosquito seeking host. MHSA can find near-optimum solutions for the traveling salesman problem (TSP), however there are two drawbacks. First, it may be trapped into local optimum. Second, the solution exists several circles sometimes. In this paper, we adopt the Random Walk and the Game of Life strategies to improve MHSA, and propose a Random Walk and Game of Life Host-seeking Algorithm (RGHSA). RGHSA model is proposed to solve these two drawbacks. We use set theory and probability theory to prove the validity of the model. TSPlib is a benchmark for TSP. In the simulation, we choose server datasets from TSPlib, and compare the simulation result of RGHSA with original MHSA, Simulated Annealing Algorithm (SA) and Ant Colony Optimization Algorithm (ACO). The result shows that RGHSA have a good performance in TSP. ? Springer International Publishing AG, part of Springer Nature 2018.