南京大学学报(自然科学版) ›› 2010, Vol. 46 ›› Issue (1): 1–9.
• • 下一篇
魏建香 1 ,2 , 孙越泓 3 , 苏新宁 1
Wei J ian 2 Xiang 1 , 2 , S un Yue 2 Hong 3 , S u Xin 2 N ing 1
摘要： 粒子群算法是一种新的群体智能算法 , 被广泛用于各种复杂优化问题的求解 , 但算法存在着过早收敛问题 . 为了克服算法早熟的缺点 , 将粒子群看作是一个复杂的免疫系统 , 借鉴生物学中免疫系
统自我调节的机制 , 提出了一种新的基于免疫选择的粒子群优化算法(IS 2 PSO) . 免疫系统中的抗原、 抗体和亲和度分别对应了待优化函数的最优解、 候选解和适应度 . IS 2 PSO 通过免疫算法中免疫记忆、 疫苗
接种、 免疫选择等操作有效地调节 PSO 算法中种群的多样性 . 给出了算法的详细步骤 , 并将本文提出的算法与基本的粒子群算法 (bPSO) 在几个典型 Benchmark 函数的优化问题应用中进行了比较 , 仿真结果
表明 :IS 2 PSO 算法可以有效避免早熟问题 , 提高粒子群算法求解复杂函数的全局优化性能 .
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