Parallel immune clone algorithm is proposed based on population coevolution theory and parallel computing affinity of individual at multiple compute nodes. Introducing the immune memory mechanism, the evolution processes of antibody population and memory units are conducted simultaneously, meanwhile, it improves mutual cooperation among antibodies, and ensures solution set approaching optimal solution from the inside of feasible region or infeasible region border. Clone proliferation, high frequency variation and operation of crossover operators increase the chance that better individuals gain affinity maturation by the operation of clone expansion, improve diversity of antibody population distribution, achieve the balance of optimization between depth and range, and ensure the convergence of the algorithm and the diversity of the search range. A computational study for a standard data set is carried out to test the validity of the algorithm, and the effect of algorithm parameters on the results is analyzed. The simulation results show that the global search capability, local search capability, algorithm stability and computing speed of the algorithm are all superior to conventional optimization algorithms such as normal immune clone optimization algorithm, genetic algorithm, etc.