摘要
为了解决粘性二进制粒子群算法在优化过程中易陷入局部最优、全局搜索能力弱、后期收敛性能差的弊端,提出了一种非线性因子的粘性二进制粒子群算法(NFSBPSO)。NFSBPSO算法采用非线性递减策略优化粘性权重,平衡全局与局部探索能力;同时,利用混沌策略对种群进行初始化,在每个迭代过程中对最差和最优粒子进行优化,以提高种群质量,扩大种群的全局探索能力。将新算法与3个对比算法在基准函数上进行测试,实验表明新的算法能较好地跳出局部最优,提高了算法的稳定性和收敛能力。
In order to solve the disadvantages of sticky binary particle swarm optimization,which is easy to fall into local optimum,weak global search ability and poor late convergence performance in the process of optimization,a nonlinear factor sticky binary particle swarm optimization(NFSBPSO)is proposed.NFSBPSO algorithm uses nonlinear decreasing strategy to optimize the sticky weight and balance the global and local exploration ability.At the same time,the chaos strategy is used to initialize the population,and the worst and best particles are optimized in each iteration process to improve the quality of the population and expand the global exploration ability of the population.The new algorithm is tested on the benchmark function with three comparison algorithms.The experimental results show that the new algorithm can jump out of the local optimum better and improve the stability and convergence ability of the algorithm.
作者
程倩倩
CHENG Qianqian(Taiyuan Normal University,Jinzhong 030619,China)
出处
《现代信息科技》
2023年第3期61-65,共5页
Modern Information Technology
关键词
非线性因子
混沌策略
粒子优化
粘性二进制粒子群算法
nonlinear factor
chaos strategy
particle optimization
sticky binary particle swarm optimization
