Updated Particle Swarm Optimization for Mechanical Engineering Design Optimization

Abstract

In the optimization area, particle swarm optimization is an excellent optimization algorithm. It influenced thru position and velocity update of the particles. Particles velocity update significantly depend on its factor inertia weight (with previous velocity) and acceleration coefficients (with cognitive and social components). Also, PSO has various advantages like simplicity and fast convergence. However, PSO easily trapped into local optima due to simple search procedure. To improve the presentation of PSO, this article advises an updated-PSO (U-PSO) for solving mechanical engineering design optimization problems. In U-PSO, novel parameters (inertia weight, acceleration coefficients and lively coefficient) for velocity update and position update equation are employed to retain exploitation and exploration. To verify the efficiency and robustness, proposed U-PSO applied to solve two complex mechanical engineering designs optimization problems. The experimental outcomes validate that the suggested U-PSO has significant advantages over the compared algorithms.