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中文摘要：
针对遗传算法中存在的早熟收敛和后期收敛速度慢的问题，分析传统的小生境遗传算法和多种群遗传算法的特点和不足，提出基于聚类的伪并行遗传算法．当种群进化到一定程度后，进行聚类分析．在各个聚类内部，利用局部搜索算法获得极值点．其余未分类个体与聚类代表元按照小生境技术进一步搜索，从而获得较好的全局探索能力．从理论上证明该算法的收敛性．采用典型函数进行实例计算，并与杰出保留遗传算法、确定性排挤遗传算法和传统的多种群遗传算法的性能进行比较，结果表明本文算法的有效性．
英文摘要：
The traditional genetic algorithm （GA） for multi-modal function optimization is studied and the characteristics of Niche GA and multi-population GA are analyzed. A clustering based pseudo-parallel genetic algorithm is proposed. Cluster analysis is carried out on all the individuals. Local search algorithm is used to search the optimum in all clusters. A new subpopulation is created by the unclassified individuals and the representations of all clusters. To get better global search capacity, niche technology is applied in the subpopulation. The convergence of the algorithm is proved theoretically. Moreover, a new method is designed for automatically calculating clustering threshold. Finally, the presented algorithm is compared with EGA, DCGA and MPGA. Results show that the new algorithm is well in searching global optimum and maintaining population diversity.
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