Abstract: Packing and layout problems belong to NPC problem theoretically and they have extensive engineering applications practically. Parallel genetic algorithm (PGA) is relatively effective to solve this kind of problems. But there still exist two main defects, I.e. premature convergence and slow convergence rate. To overcome them, a parallel hybrid immune algorithm (PHIA) is proposed based on PGA. Introducing immunity theory into parallel genetic algorithm has double functions. One is that immune selection operator can prevent the algorithm from premature. The other is that convergence rate can be accelerated by individual migration strategy between subpopulations based on immune memory mechanism. In this algorithm, chaos initialization, adaptive crossover and mutation operators are adopted. And subpopulations are classified as several types according to the values of crossover and mutation probability. To be hybridized with Powell method can further improve local searching performance of the algorithm. Two examples that originate from the layout design of satellite module and printed circuit board (PCB) show that PHIA is feasible and effective.

Key words: Genetic algorithm, Hybrid methods, Immune function, Layout design, Satellites