Open AccessImproved Chicken Swarm Optimization Algorithm to Solve the Travelling Salesman Problem
Author(s) -
Fayçal Chebihi,
Mohammed Essaid Riffi,
Amine Agharghor,
Soukaina Cherif Bourki Semlali,
Abdelfattah Haily
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v12.i3.pp1054-1062
Subject(s) - metaheuristic , travelling salesman problem , swarm behaviour , mathematical optimization , swarm intelligence , parallel metaheuristic , robustness (evolution) , algorithm , benchmark (surveying) , computer science , multi swarm optimization , optimization problem , mathematics , particle swarm optimization , meta optimization , biochemistry , chemistry , geodesy , gene , geography
This paper proposes a novel discrete bio-inspired chicken swarm optimization algorithm (CSO) to solve the problem of the traveling salesman problem (TSP) which is one of the most known problems used to evaluate the performance of the new metaheuristics. This problem is solved by applying a local search method 2-opt in order to improve the quality of the solutions. The DCSO as a swarm system of the algorithm increases the level of diversification, in the same way the hierarchical order of the chicken swarm and the behaviors of chickens increase the level of intensification. In this contribution, we redefined the basic different operators and operations of the CSO algorithm. The performance of the algorithm is tested on a symmetric TSP benchmark dataset from TSPLIB library. Therefore, the algorithm provides good results in terms of both optimization accuracy and robustness comparing to other metaheuristics.