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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 Immune function Hybrid methods Layout
design Satellites
CLC No: TP391.72
TP301.6
国家自然科学基金(50275019，50175009，60073036)和教育部博士点专项研究基金(20010141005)资助项目.
Received 20020920, received in revised form 20030128
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