【作者】 王翠茹； 江将；

【Author】 WANG Cui-ru JIANG Jiang Dept. of computer science and technology, North China Electrical Power University, Baoding, 071003, China

【机构】 华北电力大学计算机科学与技术学院；

【摘要】 为了更好地解决函数优化的问题,弥补传统寻优方法的不足,将粒群优化算法以及模拟退火的思想引入免疫遗传算法中,并定义了基于距离测度的个体浓度,并以此来自适应地确定其交叉概率和变异概率,形成一类新的混合免疫遗传算法。有效地提高了算法的收敛速度,同时,保持了个体的多样性,并通过模拟退火操作避免了陷入局部最优,有效地提高了算法性能。将其应用到几个典型函数的寻优过程中,实验证明,该算法具有较好的收敛性及较快的收敛速度。更多还原

【Abstract】 In order to make up for the disadvantage of the genetic algorithm, and optimize the function with better solution, we combine the particle swarm optimization and the algorithm of simulate anneal with the immune genetic arithmetic, and define the strength based on distance measurement. The probabilities of crossover and mutation are adaptively determined according to the distance measurement. Then an advanced and adaptive algorithm was introduced in this paper. The advantages are via immune operation to maintain diversity of antibodies, and the speed of convergence is improved effectively. Avoiding the immersion into the part optimization .The performance of the algorithm is advanced by a long way. This algorithm is used to solve function optimization problem with boundary constraints. Computation results show that the algorithm is effective.更多还原