摘要
将局部寻优能力极强的人工Hopfield神经网络算法融合到粒子群优化算法的搜索过程中,提出解决一类0/1优化问题融合神经网络的混合粒子群优化算法。在该算法中依粒子群当前全局最优个体为初始态激活神经网络,生成一个局部最优态,用这个局部最优态代替粒子群当前全局最优个体,增强了算法的局部寻优能力,通过数值试验证明该算法是有效的。
A hybrid PSO algorithm was proposed, where the Hopfield manpower neural network with better local searching ability was combined with PSO for solving a class of 0/1 knapsack problem. The current global optimum chromosome activated the neural network and obtained a local optimum state that was used to replace the current global optimum chromosome in this algorithm. Local optimization ability of the algorithm was strengthened. Numerical test shows that this algorithm is effective.
出处
《计算机应用》
CSCD
北大核心
2008年第6期1559-1562,共4页
journal of Computer Applications
基金
国家社会科学基金资助项目(07XJY038)
关键词
粒子群优化
神经网络
0/1优化问题
Particle Swarm Optimization （PSO）
neural network
0/1 optimization problem
