An Efficient Particle Swarm Optimization Approach Based on Cultural Algorithm Applied to Mechanical Design

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the swarm intelligence theory, this paper discusses the use of PSO approaches using an operator and based on the Gaussian probability distribution function as a population space of a cultural algorithm, called cultural Gaussian PSO (GPSO-CA). Cultural algorithms are mechanisms that incorporate domain knowledge obtained during the evolutionary process, which increase the efficiency of the search process. These approaches are employed in a well-studied continuous optimization problem of mechanical engineering design.