中文摘要：

针对复杂寻优问题,提出了一种新的遗传算法-一维智能体遗传算法（CAGA）。该算法采用一维链式智能体网络结构,实现动态邻域竞争选择、正交交叉和自适应变异,可更好的保持种群多样性,从而获得较优的优化精度。实验采用了多个多维复杂函数进行了优化实验,结果表明,该遗传算法比其他多个著名优化算法可获得更优的优化结果。

英文摘要：

According to complex optimization problem, a new genetic algorithm （one dimensional genetic algorithm） was proposed for global numerical optimization. This algorithm adopts chain-like agent structure, realizes dynamic neighboring competition strategy, orthogonal crossover operation and adaptive mutation operation to keep the diversity of the population better, thereby obtaining better optimization result. Several complex multidimensional benchmark functions were tested for the comparison of CAGA with some other well-known genetic algorithms. The experimental results show that CAGA can obtain better optimization result than some other well-known genetic algorithms.