A Surrogate-Assisted Evolutionary Algorithm for Space Component Thermal Layout Optimization

In space engineering, the electronic component layout has a very important impact on the centroid stability and heat dissipation of devices. However, the expensive thermodynamic simulations in the component thermal layout optimization problems bring great challenges to the current optimization algorithms. To reduce the cost, a surrogate-assisted evolutionary algorithm with restart strategy is proposed in this work. The algorithm is consisted of the local search and global search. A restart strategy is designed to make the local search jump out of the local optimum promptly and speed up the population convergence. The proposed algorithm is compared with two state-of-the-art algorithms on the CEC2006, CEC2010, and CEC2017 benchmark problems. The experiment results show that the proposed algorithm has a high convergence speed and excellent ability to find the optimum in the expensive constrained optimization problems under the very limited computation budget. The proposed algorithm is also applied to solve an electronic component layout optimization problem. The final results demonstrate the good performance of the proposed algorithm, which is of great significance to the component layout optimization.

Automated summary

The study proposes a surrogate-assisted evolutionary algorithm that can find the optimal solution in expensive electronic component layout optimization problems. By combining local search and global search, and designing a restart strategy, the algorithm converges to the optimal solution more quickly.