Multiobjective wind driven optimization approach applied to transformer design

Metaheuristics of the natural computing field have been proposed as an alternative to mathematical optimization approaches to address non convex problems involving large search spaces. In recent years a new optimization metaheuristic algorithm was proposed called Wind Driven Optimization (WDO). WDO is a stochastic nature-inspired paradigm based on atmospheric motion. In this paper, a modified version of WDO is proposed and evaluated, based on Levy flights (or Levy motions) to tune its control parameters, called Levy WDO (LWDO). Levy flight or anomalous diffusion process is a random walk characterized by Markov chain in which the step-lengths have a probability distribution that is heavy-tailed. To evaluate the multiobjective optimization performance of the WDO and the proposed LWDO, a benchmark for optimizing of a safety isolating transformer is adopted. In this paper, the transformer design optimization is treated as a multiobjective problem, with the aim to minimize both the total mass (iron and copper materials) and losses taking into consideration design constraints. Simulation results testify that the multiobjective LWDO is a promising approach for multiobjective optimization as it outperforms the WDO in multiobjective version and the classical NSGA-II (Non-dominated Sorting Genetic Algorithm, version II).