System optimization by multiobjective genetic algorithms and analysis of the coupling between variables, constraints and objectives

• J. Régnier ^[1] ; B. Sareni ^[1] ; X. Roboam ^[1]
  1. [1] Laboratoire d'Electrotechnique et d'Electronique Industrielle, UMR INPT‐ENSEEIHT/CNRS, Francia
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 24, Nº 3, 2005, págs. 805-820
• Idioma: inglés
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• Resumen

  • Purpose – This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGAs) for the design of electrical engineering systems. MOGAs allow one to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and sensitivity analysis by determining the evolution of design variables along the Pareto‐optimal front.

    Design/methodology/approach – To illustrate the use of MOGAs in electrical engineering, the optimal design of an electromechanical system has been investigated. A rather simplified case study dealing with the optimal dimensioning of an inverter – permanent magnet motor – reducer – load association is carried out to demonstrate the interest of the approach. The purpose is to simultaneously minimize two objectives: the global losses and the mass of the system. The system model is described by analytical model and we use the MOGA called NSGA‐II.

    Findings – From the extraction of Pareto‐optimal solutions, MOGAs facilitate the investigation of parametric sensitivity and the analysis of couplings in the system. Through a simple but typical academic problem dealing with the optimal dimensioning of a inverter – permanent magnet motor – reducer – load association, it has been shown that this multiobjective a posteriori approach could offer interesting outlooks in the global optimization and design of complex heterogeneous systems. The final choice between all Pareto‐optimal configurations can be a posteriori done in relation to other issues which have not been considered in the optimization process. In this paper, we illustrate this point by considering the cogging torque for the final decision.

    Originality/value – We have proposed an original quantitative methodology based on correlation coefficients to characterize the system interactions.