中文摘要：

提出一种基于克隆选择算法（CSA）的任务完成时间—经济成本多目标优化算法。该算法利用前驱任务优先调度策略生成合法的初始种群,避免随机解引起算法收敛速度慢的问题;在种群的进化阶段,引入交叉算子,增加了种群的差异性。与遗传算法（GA）的对比实验证明该算法在提高收敛速度和探索最优解方面性能更优。

英文摘要：

This paper proposed a multi-objective optimization algorithm concerning time and cost of tasks based on the clone selection algorithm （CSA）. It generated a legal initial population by using precursor tasks priority schedule policy to avoid the algorithm converged slowly caused by random initial solution. It also introduced crossover operator during the evolutionary stage of population to increase the population diversity. The result of simulation experiment compared with GA shows that the algorithm can effectively improve the convergence speed and explore optimal solution.