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针对遗传算法在求解极复杂优化问题中出现的过早收敛、执行效率差的缺点,提出了一种改进的伪并行遗传算法.该算法将并行进化与串行搜索相结合,提高了算法的收敛速度.同时该算法通过种群因子控制伪并行算法中的各子种群的规模,不仅保证了搜索过程中勘探和开采的平衡,克服过早收敛,而且减少了计算的复杂性,特别是在处理复杂优化问题上具有较高的性能.实验结果证明了该算法的有效性.
Aiming at the shortcomings of premature convergence and poor execution efficiency of genetic algorithm in solving very complex optimization problems, an improved pseudo-parallel genetic algorithm is proposed, which combines parallel evolution with serial search to improve the convergence of the algorithm Speed.At the same time, this algorithm controls the size of each sub-population in the pseudo-parallel algorithm by means of population factors, which not only ensures the balance of exploration and mining in the search process, overcomes the premature convergence, but also reduces the computational complexity, especially in dealing with complex The optimization problem has higher performance.The experimental results show the effectiveness of the algorithm.