基于量子策略的布谷鸟搜索算法研究

Research on Cuckoo Search Algorithm Based on Quantum Mechanism

针对基本布谷鸟搜索算法存在局部搜索能力较弱、收敛速度偏慢和精度较低等缺点,文中研究了基于量子策略的布谷鸟搜索算法。借助于量子策略使布谷鸟的寻巢搜索行为具有多样性,并在此基础上提出3种改进局部搜索能力的措施:引入惯性权值、自适应减小鸟窝主人发现外来鸟蛋的概率、随机扰动增量的优化,并通过对两类基准测试函数的寻优结果对比,证明提出的改进融合算法精度更高,且具有更大的优势。

Aiming at the disadvantages of the basic cuckoo search（ CS） algorithm such as weaker local search ability,slower convergence rate and poorer optimization precision,this paper studies the quantum inspired cuckoo search algorithm. Firstly,we enabled the cuckoos with heterogeneous search behaviors towards the nests with the help of quantum mechanism. Then three measures which can improve the cuckoos＇ local search capability were introduced on this basis,namely the introduction of a similar inertia weight to the equation of renewal of the nests positions,an adaptively decreased probability of the cuckoo nests being replaced with a randomly generated new one,and the improvement of increment with a random disturbance. A graphical comparison of the original CS algorithm and the quantum one used for optimization of two kinds of benchmark functions shows that the latter algorithm possesses greater advantages over the original one with a better precision.