An imprecise Multi-Objective Genetic Algorithm for uncertain Constrained Multi-Objective Solid Travelling Salesman Problem

作者：

Highlights：

• New imprecise Multi-Objective Genetic Algorithm (iMOGA) uses fuzzy-age selection.

• Also adaptive crossover and generation dependent mutation are used in iMOGA.

• Fuzzy-extended age based selection is also used in place of fuzzy-age based selection.

• Constrained Solid TSPs are solved with random, fuzzy-random, random-fuzzy and bi-random data.

• iMOGA is tested with TSPLIB data and its supremacy also proved by ANOVA test.

摘要

•New imprecise Multi-Objective Genetic Algorithm (iMOGA) uses fuzzy-age selection.•Also adaptive crossover and generation dependent mutation are used in iMOGA.•Fuzzy-extended age based selection is also used in place of fuzzy-age based selection.•Constrained Solid TSPs are solved with random, fuzzy-random, random-fuzzy and bi-random data.•iMOGA is tested with TSPLIB data and its supremacy also proved by ANOVA test.

论文关键词：CMOSTSP,Fuzzy set based selection,Adaptive crossover,Generation dependent mutation,iMOGA

论文评审过程：Received 8 April 2015, Revised 17 October 2015, Accepted 18 October 2015, Available online 23 October 2015, Version of Record 18 November 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.10.019