Hybrid Simulated Annealing with Particle Swarm Optimization Applied Krigging Meta Model

[Abstract] This paper is concerned with the optimization of composite housing in a multi-spectral camera using KSAPSO (Kriging-Simualted Annealing-Particle Swarm Optimization). The effective use of KSAPSO on physical problems has been expanded to provide global approximations for optimization problems. KSAPSO is a hybrid approximation algorithm. In the KSAPSO, simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum and particle swarm optimization (PSO) combines global search and local search to search for the optimal results. And in order to reduce computational costs in calculating design problems, the Kriging is used in this study. Kriging is related to the experimental designs that provide maximum information by minimum number of design experiments. The proposed methodology is applied to the design of a Multi-Spectral Camera (MSC), as a practical example, which will provide high resolution panchromatic and multi-spectral images and is carried by a satellite designed to fulfill the need for further Earth observation and allowing scientists and communication experts to conduct potentially valuable experiments. When this composite structure is optimized, design constraints are taken for natural frequency and shear stress which should be considered in a launching environment. I. Introduction Today’s engineering design problems become more complex. Therefore, approximate methods such as Kriging and response surface method are used extensively in engineering applications for a variety of tasks because of the flexibility to approximate many different and complex response functions. 1 Consequently, diverse optimization methods using Kriging are offered. In this paper, an improved probabilistic algorithm is proposed for practical optimal designs by KSAPSO. The effective use of KSAPSO on physical problems has been expanded to provide global approximations for optimization problems. KSAPSO is a hybrid approximation algorithm. This hybrid approach makes full use of the strong global search ability of PSO and the strong local search ability of SA and offsets the weaknesses of each other. In order to reduce computational costs in calculating design problems, the Kriging used in this study is related to the experimental designs that provide maximum information by minimum number of design experiments. This paper typically proposes central composite design (CCD) as the classical experimental designs and uniform design (UD) as space filling experimental designs. 2 Moreover, the sequential approximate procedures by a stochastic algorithm is proposed to help the global optimum (chosen from the local optimum values) to move to higher probabilistic design domain. In this paper, the stochastic process is combined with the Chebyshev inequality, probability of success (POS) that simultaneously satisfies all constraints and objective limit. The optimization by KSAPSO is applied to the design of a composite housing of Multi-Spectral Camera (MSC) which is satellite equipment. MSC is designed for further Earth observation, and will provide high resolution panchromatic and multi-spectral images. Satellite components are too expensive and hard to repair on operation. High reliability for the satellite components is required under launching and space environments for steady functioning. In this study, discrete design variables are the ply thicknesses of composite housing. Design constraints are taken for natural frequency and shear stress that should consider on the design of satellite in launching situation.