布谷鸟优化算法改进及与粒子群算法融合研究.pdf

布谷鸟优化算法改进及与粒子群算法融合研究

摘 要

优化问题与人类的生活是息息相关的。最近几年，优化问题已广
泛地应用于日常生活和工程计算等领域。传统的数值计算方法已经不
能满足越来越复杂的优化问题。因此，研究者转变为依赖于元启发式
算法求解复杂的优化问题，例如遗传进化算法、粒子群算法、差分进
化算法等。本文针对优化问题，对布谷鸟算法进行了改进研究。主要
工作如下：
。由于布谷鸟算法完全依赖于随
机行走策略，使其不能有较快的收敛速度，并且随着迭代步骤的增加，
搜索活性下降，因此我们给出了一种新的自适应布谷鸟搜索算法。在
该算法中，提出了一种自适应参数控制策略来动态地调整布谷鸟算法
参数，增加了布谷鸟算法的搜索性能。此外，给出了一种特殊的变异
策略，与布谷鸟算法结合，进一步改善了布谷鸟的种群多样性。最后，
通过几个经典函数测试表明，算法不仅提高了其搜索精度，而且加快
了布谷鸟算法的收敛速度。
。为了改善粒子群算法易出现
早熟的问题，提出了一种基于莱维飞行的粒子群优化算法。该算法在
粒子更新完位置后，不直接计算目标函数值，而是利用莱维飞行改变
粒子的移动方向，防止粒子陷入局部最优值。仿真实验表明，该算法
具有更好的优化效果。



关 键 词：布谷鸟算法；自适应；变异策略；粒子群算法；莱维飞行；



I
Improving Cuckoo Algorithm and Particle Swarm Algorithm
fusion research

ABSTRACT
The optimization problems are always closely related to the real world life. In
recent years, optimization problems have been widely applied to the field of daily life
and engineering calculation. Modern optimization problems with growing complexity
pose a great challenge to the traditional numerical calculation methods. Therefore,
researchers turn to solve complex optimization problems by relying on heuristic
algorithm, such as genetic algorithm (GA), particle swarm optimization algorithm
(PSO), differential evolution algorithm (DE) and so on. For the optimization problems,
this paper made some modifications to the Cuckoo Search (CS) algorithm and PSO. The
main work is as follows:
1. A new Self adaptive Cuckoo Search Algorithm. The cuckoo algorithm
completely depends on the random walk strategy, so that it does not have a fast
convergence speed, and with the increase of the iterative steps, the search activity will
decrease. Thus, we proposed a new adaptive cuckoo search algorithm. In the a