This paper presents a new method based on Equilibrium Optimizer (EO) algorithm that is inspired from the mass balance of a control volume for traveling salesman problem (TSP). For enhancing the efficiency of EO, the 2opt movement algorithm is used to update the solution generated by EO. The efficiency of the proposed EO for the TSP problem has been compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on different instances consisting of the 14-city, 30-city, 48-city and 52-city. The calculated results show that for the large scale instances such as 48-city and 52-city, EO has found the better tour than PSO. In comparison with GA, EO has ability finding the best tour with the smaller mean and standard deviation. The comparisons with previous methods in literature have also demonstrated that EO has ability to search the better tour than other methods. Thus, the proposed EO can be a potential method for the TSP problem.
This paper presents a new method based on the artificial ecosystem optimization (AEO) algorithm for finding the shortest tour of the travelling salesman problem (TSP). Wherein, AEO is a newly developed algorithm based on the idea of the energy flow of living organisms in the ecosystem consisting of production, consumption and decomposition mechanisms. In order to improve the efficiency of the AEO for the TSP problem, the 2-opt movement technique is equipped to enhance the quality of the solutions created by the AEO. The effectiveness of AEO for the TSP problem has been verified on four TSP instances consisting of the 14, 30, 48 and 52 cities. Based on the calculated results and the compared results with the previous methods, the proposed AEO method is one of the effective approaches for solving the TSP problem.
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