摘要
针对传统遗传算法存在的早熟收敛现象,提出一种基于云控制的混沌多种群自适应遗传算法。该算法兼顾全局性和个体差异性两方面平衡,通过云控制器实现交叉率和变异率的自适应调节。在种群正常进化时,对个体实行惩强扶弱措施,在发生早熟收敛或有早熟收敛趋势时,对劣质个体实行灾变,同时采用多种群优化机制实现种群之间的同步进化。实验结果表明,与标准遗传算法和自适应遗传算法相比,该算法能够有效地避免早熟收敛问题,具有较高的收敛效率。
Aiming at the phenomenon of premature convergence in traditional genetic algorithm, an adaptive genetic algorithm with chaos and multi-population based on cloud control is proposed. Keeping the balance between global condition and individual difference, crossover rate and mutation rate are adaptively adjusted by the cloud control. An individual measure on punishing the strong and helping the weak is taken when the evolution is normal, while the algorithm is premature convergence, the inferior individuals is performed. Additionally, the proposed algorithm adopts multi-16opulation optimization mechanism to realize the evolution of each population simultaneously. Experimental results show that, compared with the Standard Genetic Algorithm（SGA） and the Adaptive Genetic Algorithm （AGA）, the proposed algorithm can effectively avoid the problem of premature convergence, obtain a higher convergence efficiency.
出处
《计算机工程》
CAS
CSCD
2014年第1期186-190,共5页
Computer Engineering
基金
中央高校基本科研业务费专项基金资助项目(201121302020010)
教育部人文社科基金资助项目(10YJC630395)
广东省自然科学基金资助项目(9151027501000039)
广东高校优秀青年创新人才培养计划基金资助项目(2012LYM_0092)
关键词
遗传算法
云模型
自适应技术
函数优化
混沌初始化
多种群
genetic algorithm
cloud model
adaptive technology
function optimization
chaos initialization
multi-population
