Random update particle swarm optimizer (RUPSO): A novel robust optimization algorithm

Achieving an optimal design, especially those with numerous variables, is a tedious process. It is regularly achieved by mathematical programming or Metaheuristic algorithm methods. A novel metaheuristic optimization algorithm is presented in this research. The proposed algorithm is based on the well-known Particle Swarm Optimization (PSO) algorithm, in which the updating process has been improved. The efficiency of the new method, named Random Update Particle Swarm Optimizer (RUPSO), has been verified for problems with continuous variables as well as problems with discrete variables. It is shown that RUPSO converges to better optimum solutions than PSO and other existing metaheuristic algorithms. It is for its capability of searching through the entire feasible search spaces and going over the local optima. Random updating process, which has been utilized in RUPSO, decreases the sensitivity of the algorithm and increases its stability in solving optimization problems. High stability of the method is also shown in the paper. The RUPSO algorithm finds the optimized answers with a very high convergence rate for problems with discrete or even continuous variables.

Automated summary

A novel metaheuristic optimization algorithm called RUPSO is proposed in this research, based on the well-known Particle Swarm Optimization algorithm. The RUPSO algorithm has been shown to converge to better optimum solutions compared to PSO and other existing metaheuristic algorithms. The random updating process used in RUPSO decreases the sensitivity of the algorithm and increases its stability, allowing it to find optimized answers with high convergence rates for problems with discrete or continuous variables.