Non-dominated sorting gravitational search algorithm for multi-objective optimization of power transformer design
Mohamed Zellagui, Heba A. Hassan, Almoataz Youssef Abdelaziz · University of Zagreb University Computing Centre (SRCE) · 2017
Transformers are crucial components in power systems.Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower mass and losses.Multi-objective Optimization Problems (MOPs) consist of several competing and incommensurable objective functions.Recently, as a search optimization technique inspired by nature, evolutionary algorithms have been broadly applied to solve MOPs.In this paper, a power Transformer Design (TD) methodology using Non-dominated Sorting Gravitational Search Algorithm (NSGSA) is proposed.Results are obtained and presented for NSGSA approach.The obtained results for the study case are compared with those results obtained when using other multi objective optimization algorithms which are Novel Gamma Differential Evolution (NGDE) Algorithm, Chaotic Multi-Objective Algorithm (CMOA), and Multi-Objective Harmony Search (MOHS) algorithm.From the analysis of the obtained results, it has been concluded that NSGSA algorithm provides the most optimum solution and the best results in terms of normalized arithmetic mean value of two objective functions using NSGSA to the TD optimization.