摘要
为了解决规模复杂的旅行商问题,提出了融合蚁群算法和粒子群算法的一种群体智能混合算法,并引入了人工免疫算法。为了克服规模较大的TSP问题易陷入局部最优的缺点,在ACO-PSO混合算法中添加交叉与变异、接种疫苗和免疫选择等过程,使其具有较强的全局寻优能力和较好的搜索收敛性。一方面利用其噪声忍耐、自学习、自组织和记忆功能,可以帮助效仿更好的精英蚂蚁,保证了蚂蚁的进化速度;另一方面,则是利用免疫算法具有多样性、快速和随机搜索,达到全局搜索的效果。通过大量仿真实验数据对比表明,改进的混合算法搜索结果好于类似算法,并运用在TSP问题上,取得了很好的效果。
In order to solve the complex scale problem of traveling salesman,this paper puts forward a kind of swarm intelligent hybrid algorithm of combination of ant colony algorithm and particle swarm algorithm and introduces artificial immune algorithm. In order to overcome the problem of large scale TSP apt to be trapped in local optima,it adds crossover and mutation,vaccination and immune selection process,which has powerful ability of global searching and better search convergence. On one hand,by the use of its noise tolerance,self-learning,self-organization and memory function,it can help to follow to ensure better elitist ants and improve ant evolution speed; on the other hand,it uses immune algorithm with diversity,rapid and random search to achieve the global search performance. The simulation experimental data comparison shows that,the improved hybrid algorithm searches results better than the similar algorithms,and it has satisfactory results applied in large scale TSP.
出处
《信息技术》
2016年第5期162-165,170,共5页
Information Technology
关键词
人工免疫算法
蚁群算法
粒子群算法
群体智能
精英蚂蚁
旅行商问题
artificial immune algorithm
ant colony algorithm
particle swarm algorithm
swarm intelligence
elistist ants
traveling salesman problem
