Particle Swarm Optimization Algorithm Based on Semantic Relations and Its Engineering Applications

Abstract

Particle swarm optimization algorithm (PSO) is a good method to solve complex multi-stage decision problems. But this algorithm is easy to fall into the local minimum points and has slow convergence speed, According to the semantic relations, an improved PSO algorithm has been proposed in this paper. In contrast with the traditional algorithm, the improved algorithm is added with a new operator to update its crucial parameters. The new operator is to find out the potential semantic relations behind the history information based on the ontology technology. Particle swarm optimization can be applied to many engineering fields, taking Traveling Salesman Problem (TSP) as example. Our experiments show accuracy of the improved particle swarm algorithm that is superior to that obtained by the other classical versions, and better than the results achieved by the compared algorithms, besides, this improved algorithm can also improve the searching efficiency.