摘要
针对粒子群算法易陷入"局部最优解"和搜索精度逐渐降低的缺点,提出了基于交叉和自适应权重的混合粒子群优化算法。加入的交叉操作使得种群在粒子数目不变的情况下多样性得以维持,而自适应权重有效地平衡了整个算法的全局与局部搜索能力。通过函数测试实验表明,新的算法能够避免早熟收敛问题,有效地提高了其寻优能力。
To overcome the disadvantage of PSO such as easily getting into the local extremum and the lower search accuracy,hybrid particle swarm optimization with crossover and adaptive weight is proposed.Crossover operation can make the particle swarm maintain diversity and adaptive weight effectively balances the ability of global and local search of the whole algorithm.The result of the experiment shows that the novel algorithm can avoid the premature convergence effectively and search capability is better.
出处
《信息技术》
2010年第11期146-148,共3页
Information Technology
关键词
粒子群优化算法
交叉
惯性权重
自适应
particle swarm optimization algorithm
crossover
inertia weight
self-adaptation
