Particle Swarm Optimization with Partial Search to Solve Traveling Salesman Problem

Abstract

The traveling salesman problem (TSP) is well-known combinatorial optimization problem. TSP requires to find the shortest circular tour visiting every city exactly once from a set of given cities. TSP is the most famous combinatorial problem and interest grows in recent years to solve it new ways. Almost every new approach for solving engineering and optimization problems has been tested on the TSP as a general test bench. Recently nature inspired population based methods including PSO has drawn great attraction to solve TSP. in this thesis introduce a Particle Swarm Optimization (PSO) base algorithm to solve TSP in different way which is defined as Velocity Tentative Particle Swarm Optimization (VTPSO). Existing method introduced the idea of Swap Operator (SO) and Swap Sequence (SS) in PSO to handle TSP. In TSP, each particle represents a complete tour and velocity is measured as a SS consisting with several SOs. A SO indicates two positions in the tour that might be swapped. In the existing method, a new tour is considered after applying a complete SS with all its SOs. Whereas, every SO implantation on a particle (i.e., a solution or a tour) gives a new solution and there might be a chance to get a better tour with some of SOs instead of all the SOs. The objective of the study is to achieve better result introducing using such partial search option for solving TSP. The proposed Velocity Tentative Particle Swarm Optimization (VTPSO) algorithm is shown to produce optimal solution within a less number of generations than Self-Tentative PSO (STPSO) and Swap Sequence based PSO (SSPSO) in solving several benchmark TSP problem