CAJ | 학술논문

针对标准差分进化算法易早熟的缺点，模拟人类社会民族融合的进化历程，提出了动态种群差分进化算法（DPDE）。算法中将种群分为多个独立的子种群，子种群之间采用相互移民来进行信息交换，设置种群分裂和融合的条件来动态控制子种群个数。通过数值实验用几种典型的测试函数对DPDE的搜索性能进行了测试，实验结果表明，该算法能有效地避免早熟，具有良好的全局收敛性。
침대표준차분진화산법역조숙적결점，모의인류사회민족융합적진화역정，제출료동태충군차분진화산법（DPDE）。산법중장충군분위다개독립적자충군，자충군지간채용상호이민래진행신식교환，설치충군분렬화융합적조건래동태공제자충군개수。통과수치실험용궤충전형적측시함수대DPDE적수색성능진행료측시，실험결과표명，해산법능유효지피면조숙，구유량호적전국수렴성。
Since the standard differential evolution is easy to fall into premature convergence, the paper presented a Dynamic Population Differential Evolution Algorithm （DPDE） which was based on simulating the evolution developing history of human races. The population was divided into several subpopulations in DPDE, and the numbers of subpopulations was dynamically controlled by merging and dividing, which could enhance the searching capacity of the algorithm. Finally, three experiments were made on benchmark functions. The results show that DPDE has a good global searching capacity than DE and can avoid premature convergence.