This paper proposed an improved version of the Particle Swarm Optimization (PSO) approach to solve Traveling Salesman Problems (TSP). This evolutionary algorithm includes two phases. The first phase includes Fuzzy C-Means clustering, a rule-based route permutation, a random swap strategy and a cluster merge procedure. This approach firstly generates an initial non-crossing route, such that the TSP can be solved more efficiently by the proposed PSO algorithm. The use of sub-cluster also reduces the complexity and achieves better performance for problems with a large number of cities. The proposed Genetic-based PSO procedure is then applied to solve the TSP with better efficiency in the second phase. The proposed Genetic-based PSO procedure is applied to TSPs with better efficiency. Fixed runtime performance was used to demonstrate the efficiency of the proposed algorithm for the cases with a large number of cities.
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics