计算机与应用化学
計算機與應用化學
계산궤여응용화학
COMPUTERS AND APPLIED CHEMISTRY
2013年
4期
406-410
,共5页
布谷鸟搜索%Lévy 飞行%共轭梯度%全局寻优%收敛能力
佈穀鳥搜索%Lévy 飛行%共軛梯度%全跼尋優%收斂能力
포곡조수색%Lévy 비행%공액제도%전국심우%수렴능력
cuckoo search%Lévy flights%conjugate gradient%global searching%convergence ability
布谷鸟搜索算法(Cuckoo Search, CS)是基于群体智能的新型随机全局优化算法，具有控制参数少、搜索路径优和全局寻优能力强等优点，但也存在局部搜索能力较弱、收敛速度偏慢和收敛精度不够高等缺点。为了克服 CS 算法的缺点，提出一种基于共轭梯度的布谷鸟搜索算法(CGCS)，使经过 Lévy 飞行机制和淘汰机制进化后的布谷鸟种群沿着相互共轭的方向迅速下降，从而在保持算法的强大全局寻优能力的基础上大幅提高算法的收敛能力。用4个典型测试函数分别对 CGCS 算法和 CS 算法进行性能测试，结果表明，CGCS 算法比 CS 算法具有更快的收敛速度、更高的收敛精度和更稳定的优化结果。CGCS 算法同时具有很强的全局寻优能力、收敛能力和鲁棒性，特别适合多峰及高维函数的优化。
佈穀鳥搜索算法(Cuckoo Search, CS)是基于群體智能的新型隨機全跼優化算法，具有控製參數少、搜索路徑優和全跼尋優能力彊等優點，但也存在跼部搜索能力較弱、收斂速度偏慢和收斂精度不夠高等缺點。為瞭剋服 CS 算法的缺點，提齣一種基于共軛梯度的佈穀鳥搜索算法(CGCS)，使經過 Lévy 飛行機製和淘汰機製進化後的佈穀鳥種群沿著相互共軛的方嚮迅速下降，從而在保持算法的彊大全跼尋優能力的基礎上大幅提高算法的收斂能力。用4箇典型測試函數分彆對 CGCS 算法和 CS 算法進行性能測試，結果錶明，CGCS 算法比 CS 算法具有更快的收斂速度、更高的收斂精度和更穩定的優化結果。CGCS 算法同時具有很彊的全跼尋優能力、收斂能力和魯棒性，特彆適閤多峰及高維函數的優化。
포곡조수색산법(Cuckoo Search, CS)시기우군체지능적신형수궤전국우화산법，구유공제삼수소、수색로경우화전국심우능력강등우점，단야존재국부수색능력교약、수렴속도편만화수렴정도불구고등결점。위료극복 CS 산법적결점，제출일충기우공액제도적포곡조수색산법(CGCS)，사경과 Lévy 비행궤제화도태궤제진화후적포곡조충군연착상호공액적방향신속하강，종이재보지산법적강대전국심우능력적기출상대폭제고산법적수렴능력。용4개전형측시함수분별대 CGCS 산법화 CS 산법진행성능측시，결과표명，CGCS 산법비 CS 산법구유경쾌적수렴속도、경고적수렴정도화경은정적우화결과。CGCS 산법동시구유흔강적전국심우능력、수렴능력화로봉성，특별괄합다봉급고유함수적우화。
Cuckoo search algorithm is a novel stochastic global optimization algorithm based on swarm intelligence, with advantages of few control parameters, optimal search path and good global search capability, but it also has shortcomings of weak local search ability, slow convergence velocity and low convergence accuracy. In order to overcome these disadvantages of CS algorithm, an improved cuckoo search algorithm based on conjugate gradient is introduced. After evolved from Lévy flights and elimination mechanism, the cuckoo populations decline rapidly in the mutually conjugate directions so that the convergence ability of algorithm is strengthened significantly under the condition of maintaining the strong global search capability of CS algorithm. The CGCS algorithm and CS algorithm are tested by four typical test functions. The conclusions indicate that CGCS algorithm has faster convergence velocity, higher convergence accuracy and more stable optimization results. Meanwhile CGCS algorithm has good global search capability, convergence ability and robustness, which is particularly suitable for the optimization of multimodal function and high dimension function.
