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An Improved Vantage Point Bees Algorithm to Solve Combinatorial Optimization Problems from TSPLIB
Author(s) -
Zeybek Sultan,
Ismail Asrul Harun,
Hartono Natalia,
Caterino Mario,
Jiang Kaiwen
Publication year - 2021
Publication title -
macromolecular symposia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 76
eISSN - 1521-3900
pISSN - 1022-1360
DOI - 10.1002/masy.202000299
Subject(s) - travelling salesman problem , combinatorial optimization , benchmark (surveying) , initialization , operator (biology) , mathematical optimization , computer science , point (geometry) , local search (optimization) , algorithm , 2 opt , mathematics , geography , biochemistry , chemistry , geometry , geodesy , repressor , transcription factor , gene , programming language
This paper presents an improved version of the Vantage Point Bees Algorithm (VPBA‐II), which is implemented to solve the Travelling Salesman Problem. The Vantage Point Tree has been used to produce initial tour solutions and also as a global search operator of the proposed algorithm to find the minimal Hamiltonian tour of the Travelling Salesman Problem. VPBA‐II is tested on 15 different benchmark datasets from TSPLIB, particularly for the high dimensional combinatorial solution spaces, and it outperformed the basic Bees Algorithm. The composition of the local search operators combined with Vantage Point Tours perform better except one dataset and achieved optimum results according to best‐known solutions of Travelling Salesman Problem as a best‐case scenario. The experiments prove that Vantage Point Tour construction could be used as initialization and global search operator to improve the basic Bees Algorithm performance on the combinatorial domains.