Particle Swarm Methodologies for Engineering Design Optimization

Abstract

Particle swarm methodologies are presented for the solution of constrained mechanical and structural system optimization problems involving single or multiple objective functions with continuous or mixed design variables. The particle swarm optimization presented is a modified particle swarm optimization approach, with better computational efficiency and solution accuracy, is based on the use of dynamic maximum velocity function and bounce method. The constraints of the optimization problem are handled using a dynamic penalty function approach. To handle the discrete design variables, the closest discrete approach is used. Multiple objective functions are handled using a modified cooperative game theory approach. The applicability and computational efficiency of the proposed particle swarm optimization approach are demonstrated through illustrate examples involving single and multiple objectives as well as continuous and mixed design variables. The present methodology is expected to be useful for the solution of a variety of practical engineering design optimization problems.

Optimization design of the key parameters of McPherson suspension systems using generalized multi-dimension adaptive learning particle swarm optimization

“…PSO has demonstrated its strength in many types of single- and multi-objective optimization designs, including vehicles, aircraft, and manufacturing facilities over other optimization algorithms. 8,14–17 PSO does not require the optimization function to be differentiable, invertible, and continuous. Moreover, this algorithm is simple, efficient, robust, and demonstrates rapid convergence.…”