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人工鱼群算法是一种收敛速度快、全局优化能力强的新型群智能算法。然而,在基本鱼群算法的应用中发现:在迭代前期,算法具有较强的搜索能力;但在运行后期,其搜索能力减弱,易陷入局部极值,且搜索到的最优解精度不高。针对上述弱点,提出对可视域和步长采用自适应变化策略,引入变异算子策略,通过消亡操作对部分个体进行重新初始化或变异,对基本鱼群算法进行改进,并以函数优化和多维变量的非线性优化问题为例进行了实验研究。结果表明:改进后的人工鱼群算法具有较好的优化效果。
Artificial fish swarm algorithm is a new type of swarm intelligence algorithm with fast convergence and strong global optimization. However, it is found in the application of the basic fish swarm algorithm that the algorithm has a strong search capability in the early iteration, but its searching ability is weakened in the later stage of operation, easily fall into the local extremum, and the precision of the searched optimal solution is not high . In view of the above weakness, this paper proposes a strategy of adaptive change to the visual field and step size, introduces mutation operator strategy, re-initializes or mutates some individuals through extinction operation, improves the basic fish swarm algorithm, The nonlinear optimization problem of variables is studied experimentally. The results show that the improved artificial fish swarm algorithm has better optimization results.