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研究了用混合遗传算法求解时间最优控制问题。混合遗传算法是用粒子群位移转移的思想改变遗传算法的变异规则,通过记录各染色体的历史最优值和种群的最优值,来修正下一代的染色体,新的算法保留了遗传算法的选择和交叉操作,保证了遗传算法强大的全局搜索性能,该算法可求解数学优化问题。在分析时间最优控制问题已有求解方法优缺点的基础上,提出基于混合遗传算法求解时间最优控制问题的直接方法,为了利用该算法求解时间最优控制问题,将约束作为惩罚项包括在目标函数中,以此构造适应度函数。对线性阻尼振子问题进行了数值仿真,仿真实例验证了该算法的有效性。
The hybrid genetic algorithm is used to solve the optimal time control problem. Hybrid genetic algorithm uses the idea of particle swarm shift to change the genetic algorithm’s variation rule, and records the chromosome’s historical optimal value and the population’s optimal value to correct the chromosomes of the next generation. The new algorithm preserves the choice of genetic algorithm And cross-operation, to ensure the powerful global search performance of genetic algorithms, the algorithm can solve mathematical optimization problems. Based on the analysis of the advantages and disadvantages of existing methods for solving the problem of optimal control of time, a direct method based on hybrid genetic algorithm is proposed to solve the optimal time control problem. In order to solve this problem, the constraint is included as a penalty in The objective function, in order to construct the fitness function. The numerical simulation of the linear damped oscillator problem is simulated. The simulation results show the effectiveness of the proposed algorithm.