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We propose a parallel hybrid immune genetic algorithm (PHIGA) based on parallel genetic algorithms (PGA) in order to overcome some defects of them, such as premature and slow convergence rate. The global performance of the algorithm is improved by introducing immunity theory into PGA. This is revealed in the following two aspects. One is that immune selection can prevent the algorithm from premature. The other is that convergence rate can be accelerate by individual migration strategy between subpopulations based on immune memory mechanism. In this algorithm, chaos initialization and multiple subpopulations evolution based on improved adaptive crossover and mutation are adopted. To be hybridized with the complex method can further improve local searching performance of the algorithm. An example of layout design shows that PHIGA is feasible and effective.