摘要
考虑蚁群算法与粒子群算法的各自特点,在粒子群算法的基础上借鉴蚁群算法的信息素机制,对粒子群算法的速度位置更新公式重新定义,提出了一种基于蚁群混沌行为的离散粒子群算法,并将其应用到背包问题中。实验结果表明,该算法可以得到较优解。
Considering their own characteristics of ant colony algorithm and particle swarm optimization algorithm, the update equations of the speed and position of particles were redefined on the basis of PSO algorithm. A discrete particle swarm optimization algorithm based on chaotic ant behavior was proposed using the idea of pheromone refresh mechanism of ant colony algorithm for reference. Knapsack problem was used to test the performance of the algorithm. Compared with other algorithms, the results of the experiment show that the proposed algorithm can result in better profits.
出处
《计算机科学》
CSCD
北大核心
2010年第5期178-180,286,共4页
Computer Science
基金
国家自然科学基金资助项目(60675043)
浙江省科技计划项目(2007C21051)
杭州电子科技大学科研启动基金项目(KYS09150543)资助
关键词
信息素机制
混沌
离散粒子群
背包问题
Pheromone mechanism Chaotic Discrete particle swarm optimization Knapsack problem
