Magnetoelectric (ME) CoFe2O4–Pb(Zr,Ti)O3 composite thin films have been prepared by a sol-gel process and spin-coating technique. X-ray diffraction and scanning electron microscopy reveal that there exists local aggregation or phase separation of the CoFe2O4 and Pb(Zr,Ti)O3 phases in the films. Vibrating sample magnetometer, ferroelectric test unit, and magnetoelectric measuring device were used to characterize the magnetic and ferroelectric properties, as well as the ME effect of the films. It is shown that the films exhibit both good magnetic and ferroelectric properties, as well as a ME effect. A high initial magnetoelectric voltage coefficient for the film is observed. The ME effect of the film strongly depends on the magnetic bias and magnetic field frequency.

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Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN) method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), the multi-objective genetic algorithm (MOGA) and multi-objective differential evolution (MODE), are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

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