Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP)


  • Saman M. Almufti computer science
  • Ridwan Boya Marqas computer science
  • Renas R. Asaad computer science





Swarm Intelligence (SI), Elephant Herding Optimization (EHO), U-Turning Ant Colony Optimization (U-TACO), Optimization, Symmetric Traveling Salesman Problem (STSP).


Swarm Intelligence is an active area of researches and one of the most well-known high-level techniques intended to generat, select or find a heuristic that optimize solutions of optimization problems.
Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimi-zation problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch, while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism.
U-Turning Ant colony optimization (U-TACO) is a swarm-based algorithm uses the behavior of real ant in finding the shortest way be-tween its current location and a source of food for solving optimization problems. U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm (ACO).
In this paper, a Comparative study has been done between the previous mentioned algorithms (EHO, U-TACO) in solving Symmetric Traveling Salesman Problem (STSP) which is one of the most well-known NP-Hard problems in the optimization field. The paper pro-vides tables for the results obtained by EHO and U-TACO for various STSP problems from the TSPLIB95.


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