An Improved Particle Swarm Optimization Approach for Solving the Engineering Problems

Abstract

In this paper, a new efficient approach for improving the PSO’s performance of particle swarm optimization algorithm, namely NEA-PSO, is presented. By introducing a new strategy of velocity updating. This proposed algorithm focuses on expanding the search area around the local best and global best exploited at each iteration. Thus, in the new algorithm, the progress of position updating of each individual particle in the swarm will move a longer step in comparison with the original PSO. Based on this strategy movement, the convergence rate in the proposed algorithm will be enhanced. Moreover, because of the more flexibility of exploring new search spaces, the accuracy in the NEA-PSO is also perfected. To verify the effectiveness and reliability of the NEA-PSO algorithm, the first five benchmark test functions are used as an example to demonstrate the effectiveness of the proposed algorithm. Then, NEA-PSO is applied to solve a particular engineering problem. The obtained results show that the NEA-PSO algorithm registers high reliability, and it can be seen as an alternative to the original PSO algorithm in solving optimization problems.